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The State of The Market — November 22, 2018 : $4,502.02 (-2.06%) : $0.437498 (-1.55%) : $132.47 (-3.10%) BTC XRP ETH After rebounding from a new low yesterday, the crypto market is in red today. However, the loss is very minimal. In fact, the top 20 cryptocurrencies are down by less than 5%, except for Bitcoin Cash which is down by nearly 8%. Bitcoin Cash ABC made a controversial decision to abandon Proof of Work by enabling checkpoint for every 10 blocks. This move has not gone well with both traders and the community. Bitcoin now has a major resistance at $4,600, which would give it significant momentum if it clears within the next 48 hours. In other news, the . Donors include Japanese banking giant Sumitomo Mitsui (SMBC) and the Ethereum Foundation. 42% of the world’s top 50 universities now offer at least one crypto-related class. Also, . Spain’s treasury department asked blockchain-focused firms to report their earnings and crypto assets to the nation’s tax authorities back in April. Spanish businesses are subjected to 19–23% in taxes depending on the size of the profits. University of Tokyo has raised $800,000 in donations to launch a blockchain course Spain’s tax collection department has recognized at least 15,000 cryptocurrency related businesses that have to pay taxes on their capital gains The new cryptocurrency has already got the backing of a limited number of banks, including the Central Bank of Saudi Arabia. Besides, Ripple, the company developing xRapid and the XRP asset, has begun making inroads in Saudi Arabia. The country’s National Commercial Bank is a member of RippleNet. ( ) 1) Saudi Arabia is reportedly developing its cryptocurrency and will come to market in mid-2019. Read More The attestation, dated November 16, said Circle had $127,412,240.89 as of October 31, which is slightly higher than 127,408,827 USDC tokens in circulation at the time. With USDC’s increasing popularity, it now has 165 million tokens in circulation. The audit also confirmed that Circle and Coinbase jointly issued the stablecoin through the CENTRE Consortium. Other stablecoin issuers like Gemini and Paxos have also published similar attestations from auditing firms to support their holdings. ( ) 2) Grant Thornton LLP, the U.S. branch of Grant Thornton International has attested that Circle has fiat funds to back its stablecoin USD Coin (USDC). Read More SGX has made it clear that it is not interested in passing judgments on ICOs. Instead, it wants companies to work with it. Companies looking to launch an ICO must provide legal and auditor opinions on the nature of the digital asset in question and its accounting treatment. ICO issuers would have to declare further information if needed. The tokens will be classified as “securities/capital markets products” under Singapore’s Securities and Futures Act (SFA). ( ) 3) The Singapore Exchange (SGX) has published its expectations for listed companies that are looking to raise funds through ICOs. Read More Subscribe to the Berminal Brief Newsletter Download Berminal App for Free The Berminal Website Berminal Official Telegram Berminal Twitter

Saudi Arabia To Launch Its Own Crypto Next Year, And Circle’s Auditor Attests USDC Is Fully Backed

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de;
(2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl;
(3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de;
(4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. Table of Links Abstract and Introduction
Related Work
Methodology
Experimental Design
Experimental Results
Conclusion
Ethics Statement and References III. METHODOLOGY A. System Architecture The CUP framework is based on a two-tower architecture for representation learning (one ”tower” for users, the other for items, following the prevalent architecture in neural information retrieval with query and document/passage encodings). The two towers are jointly trained, coupled by the shared loss function and ground truth. Figure 1 shows a pictorial overview. User reviews and items descriptions are fed into BERT followed by a feed-forward network to learn latent representations. Downstream, the vectors are simply compared by a dot product for scores that indicate whether the user likes an item or not. Importantly, unlike prior works, we allow the top-most layer of BERT itself to be fine-tuned as part of the end-to-end training process. The per-item text usually comprises book titles, tags like categories or genre labels, which can be coarse (e.g., “thriller”) or fine-grained (e.g., “Scandinavian crime noir”), and a short description of the book contents. The per-user text can comprise the titles and tags of her training-set books and the entirety of her review texts, which vary widely in length and informativeness, hence the need for smart text selection. In the following, we present the CUP training procedure in Subsection III-B, the test/deployment-time inference in Subsection III-C, the judicious selection of input text snippets in Subsection III-D, and the selection of negative training samples in Subsection III-E. B. Training Another optional mechanism is to process multiple chunks of bounded-length text, which could be of interest for long user reviews. In this case, the per-user representations are learned for each chunk, and there is an additional max-pooling step between the FFN and the dot product, to combine the per-chunk vectors. We use this option for comparison in experiments. As it incurs m times higher computational and energy costs, with m being the number of chunks, we do not favor it. A user-item pair is classified with score calculated by with <, > denoting dot product and σ for the sigmoid function. We use the Adam optimizer to minimize the binary cross entropy loss between predicted labels and the ground truth with sampled negatives. During training we update the topmost layer of BERT, which allows end-to-end training of all components. This is an important difference to earlier works with similar architectures, which use frozen BERT to encode user and item representations. C. Inference Prediction for Ranking. At test time, a prediction is made for user-item pairs. We encode the item description by running it through the trained network, and we compare it to the already learned user vector, which is based on the user’s training-time reviews (none for the given test item). The scores for different test items, computed by the final dot product, yield the ranking of the candidate items. This is a very efficient computation, following established practice in neural information retrieval [37]. Search-based Recommendation. In a deployed system (as opposed to lab experiments with test samples), a typical usage mode would be search-based re-ranking: a user provides context with a tag-based query or an example of a specific liked item, which can be thought of as query-by-example. The user’s expectation is to see a ranked list of recommended items that are similar to her positive sample (as opposed to recommendations from all kinds of categories). The system achieves this by first computing approximate matches to the query item (i.e., similarity-search neighbors), and then re-ranking a shortlist of say top-100 candidates. The CUP framework supports this mode, by using a light-weight BM25 retrieval model, querying all unlabeled items with the category and textual description of the positive point at hand, and keeping the top-100 highest scoring matches. D. Coping with Long and Noisy Texts For constructing user profiles from text, the simplest idea would be to concatenate all available reviews into a long token sequence. Two problems arise, though. User reviews are a noisy mix of descriptive elements (e.g., “the unusual murder weapon”), sentiment expressions (e.g., “it was fun to read”) and personal but irrelevant statements (e.g., “I read only on weekends”). Only the first aspect is helpful for content-based profiling (as the sentiment is already captured by the fact that the user likes the book). Second, the entirety of user-provided text can be too long to be fully digested by the Transformer. Even when it would fit into the token budget, the computational and energy cost is quadratic in the number of input tokens. Therefore, we tightly limit the tokens for each user’s text profile to 128, and devise a suite of light-weight techniques for judiciously selecting the most informative pieces. Variants of our methodology are to create concise profiles by generative language models upfront (e.g., using T5 or ChatGPT), or to digest multiple chunks of text with downstream aggregation by max-pooling or averaging. We include these techniques in our experimental comparisons, but emphasize that both of these alternatives have much higher computational cost and climate footprint. Our techniques for selecting the most informative parts of user reviews to construct concise profiles, are as follows: • Weighted Phrases: selected unigrams, bigrams or trigrams, ordered by descending tf-idf weights, where tf is the frequency of the phrase in all of the given user’s reviews, and idf is pre-computed on Google books n-grams to capture informativeness. • Weighted Sentences: selected sentences, ordered by descending idf weights, where a sentence’s total weight is the sum of the per-word idf weights normalized by sentence length. • Similar Sentences: selected sentences, ordered by descending similarity scores computed via Sentence-BERT [38] for comparing the user-review sentences against the sentences of the item description. To ensure that the selected set is not dominated by a single item, the sentences are picked from different items in a round-robin manner. • ChatGPT-generated Profiles: feeding all reviews of a user, in large chunks, into ChatGPT and instructing it to characterize the user’s book interests with a few short keyphrases. The output size is limited per chunk with a total budget of 128 tokens, to enforce concise profiles. • T5-generated Keywords: using a T5 model fine-tuned for keyword generation, to cast each user’s review text into a set of keywords, concatenated to create the profile. We give examples of selected profile constructions for a Goodreads and an Amazon user in Table VII. An online demo of the CUP framework is accessible on our website[2]. E. Coping with Unlabeled Data A challenge for training in the data-poor regime is how to handle the extreme skew between positive samples and unlabeled data points for sparse users. The crux in many recommender applications is that there are extremely few, if any, explicitly negative samples, such as books rated with low marks. This holds also for the datasets in this work. Therefore, we introduce and experiment with two different techniques to construct negative training samples from unlabeled data: • Uniform random samples. Under the closed world assumption (CWA), aka Selected Completely At Random, negative training points are sample uniformly from all unlabeled data. This is the baseline technique, widely used in prior works. • Weighted pos-neg samples. Prior works on PU learning [39], with positive and unlabeled data and without explicitly negative samples, is largely based on treating unlabeled points as pairs of samples, one positive and one negative with fractional weights. The weights can be based on (learned estimates of) class priors, but the extreme skew in our data renders these techniques ineffective. Instead we leverage the fact that relatedness measures between item pairs can be derived from tagging features or interaction data. We compute item-item relatedness via basic matrix factorization of the user-item matrix for the entire dataset. The relatedness of two items is set to the scalar product between their latent vectors, re-scaled for normalization between 0 and 1. Then, each originally unlabeled sample is cloned, with one instance positive with weight proportional to its average relatedness to the user’s explicitly positive points. The negative clone’s weight is set to the complement. [2] https://sirup.mpi-inf.mpg.de/ This paper is available on arxiv under CC 4.0 license. Authors: (1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de; (2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl; (3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de; (4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. This paper is available on arxiv under CC 4.0 license. Authors: (1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de; (2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl; (3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de; (4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. Table of Links Abstract and Introduction Related Work Methodology Experimental Design Experimental Results Conclusion Ethics Statement and References Abstract and Introduction Abstract and Introduction Related Work Related Work Methodology Methodology Experimental Design Experimental Design Experimental Results Experimental Results Conclusion Conclusion Ethics Statement and References Ethics Statement and References III. METHODOLOGY A. System Architecture A. System Architecture The CUP framework is based on a two-tower architecture for representation learning (one ”tower” for users, the other for items, following the prevalent architecture in neural information retrieval with query and document/passage encodings). The two towers are jointly trained, coupled by the shared loss function and ground truth. Figure 1 shows a pictorial overview. User reviews and items descriptions are fed into BERT followed by a feed-forward network to learn latent representations. Downstream, the vectors are simply compared by a dot product for scores that indicate whether the user likes an item or not. Importantly, unlike prior works, we allow the top-most layer of BERT itself to be fine-tuned as part of the end-to-end training process. The per-item text usually comprises book titles, tags like categories or genre labels, which can be coarse (e.g., “thriller”) or fine-grained (e.g., “Scandinavian crime noir”), and a short description of the book contents. The per-user text can comprise the titles and tags of her training-set books and the entirety of her review texts, which vary widely in length and informativeness, hence the need for smart text selection. In the following, we present the CUP training procedure in Subsection III-B, the test/deployment-time inference in Subsection III-C, the judicious selection of input text snippets in Subsection III-D, and the selection of negative training samples in Subsection III-E. B. Training B. Training Another optional mechanism is to process multiple chunks of bounded-length text, which could be of interest for long user reviews. In this case, the per-user representations are learned for each chunk, and there is an additional max-pooling step between the FFN and the dot product, to combine the per-chunk vectors. We use this option for comparison in experiments. As it incurs m times higher computational and energy costs, with m being the number of chunks, we do not favor it. A user-item pair is classified with score calculated by with <, > denoting dot product and σ for the sigmoid function. We use the Adam optimizer to minimize the binary cross entropy loss between predicted labels and the ground truth with sampled negatives. During training we update the topmost layer of BERT, which allows end-to-end training of all components. This is an important difference to earlier works with similar architectures, which use frozen BERT to encode user and item representations. C. Inference C. Inference Prediction for Ranking. At test time, a prediction is made for user-item pairs. We encode the item description by running it through the trained network, and we compare it to the already learned user vector, which is based on the user’s training-time reviews (none for the given test item). The scores for different test items, computed by the final dot product, yield the ranking of the candidate items. This is a very efficient computation, following established practice in neural information retrieval [37]. Prediction for Ranking. Search-based Recommendation. In a deployed system (as opposed to lab experiments with test samples), a typical usage mode would be search-based re-ranking: a user provides context with a tag-based query or an example of a specific liked item, which can be thought of as query-by-example. The user’s expectation is to see a ranked list of recommended items that are similar to her positive sample (as opposed to recommendations from all kinds of categories). The system achieves this by first computing approximate matches to the query item (i.e., similarity-search neighbors), and then re-ranking a shortlist of say top-100 candidates. The CUP framework supports this mode, by using a light-weight BM25 retrieval model, querying all unlabeled items with the category and textual description of the positive point at hand, and keeping the top-100 highest scoring matches. D. Coping with Long and Noisy Texts D. Coping with Long and Noisy Texts For constructing user profiles from text, the simplest idea would be to concatenate all available reviews into a long token sequence. Two problems arise, though. User reviews are a noisy mix of descriptive elements (e.g., “the unusual murder weapon”), sentiment expressions (e.g., “it was fun to read”) and personal but irrelevant statements (e.g., “I read only on weekends”). Only the first aspect is helpful for content-based profiling (as the sentiment is already captured by the fact that the user likes the book). Second, the entirety of user-provided text can be too long to be fully digested by the Transformer. Even when it would fit into the token budget, the computational and energy cost is quadratic in the number of input tokens. Therefore, we tightly limit the tokens for each user’s text profile to 128, and devise a suite of light-weight techniques for judiciously selecting the most informative pieces. Variants of our methodology are to create concise profiles by generative language models upfront (e.g., using T5 or ChatGPT), or to digest multiple chunks of text with downstream aggregation by max-pooling or averaging. We include these techniques in our experimental comparisons, but emphasize that both of these alternatives have much higher computational cost and climate footprint. Our techniques for selecting the most informative parts of user reviews to construct concise profiles, are as follows: • Weighted Phrases: selected unigrams, bigrams or trigrams, ordered by descending tf-idf weights, where tf is the frequency of the phrase in all of the given user’s reviews, and idf is pre-computed on Google books n-grams to capture informativeness. • Weighted Sentences: selected sentences, ordered by descending idf weights, where a sentence’s total weight is the sum of the per-word idf weights normalized by sentence length. • Similar Sentences: selected sentences, ordered by descending similarity scores computed via Sentence-BERT [38] for comparing the user-review sentences against the sentences of the item description. To ensure that the selected set is not dominated by a single item, the sentences are picked from different items in a round-robin manner. • ChatGPT-generated Profiles: feeding all reviews of a user, in large chunks, into ChatGPT and instructing it to characterize the user’s book interests with a few short keyphrases. The output size is limited per chunk with a total budget of 128 tokens, to enforce concise profiles. • T5-generated Keywords: using a T5 model fine-tuned for keyword generation, to cast each user’s review text into a set of keywords, concatenated to create the profile. We give examples of selected profile constructions for a Goodreads and an Amazon user in Table VII. An online demo of the CUP framework is accessible on our website[2]. E. Coping with Unlabeled Data E. Coping with Unlabeled Data A challenge for training in the data-poor regime is how to handle the extreme skew between positive samples and unlabeled data points for sparse users. The crux in many recommender applications is that there are extremely few, if any, explicitly negative samples, such as books rated with low marks. This holds also for the datasets in this work. Therefore, we introduce and experiment with two different techniques to construct negative training samples from unlabeled data: • Uniform random samples. Under the closed world assumption (CWA), aka Selected Completely At Random, negative training points are sample uniformly from all unlabeled data. This is the baseline technique, widely used in prior works. Uniform random samples. • Weighted pos-neg samples. Prior works on PU learning [39], with positive and unlabeled data and without explicitly negative samples, is largely based on treating unlabeled points as pairs of samples, one positive and one negative with fractional weights. The weights can be based on (learned estimates of) class priors, but the extreme skew in our data renders these techniques ineffective. Instead we leverage the fact that relatedness measures between item pairs can be derived from tagging features or interaction data. We compute item-item relatedness via basic matrix factorization of the user-item matrix for the entire dataset. The relatedness of two items is set to the scalar product between their latent vectors, re-scaled for normalization between 0 and 1. Then, each originally unlabeled sample is cloned, with one instance positive with weight proportional to its average relatedness to the user’s explicitly positive points. The negative clone’s weight is set to the complement. [2] https://sirup.mpi-inf.mpg.de/

Recommendations by Concise User Profiles from Review Text: Methodology

As a marketer, you must know about various platforms that are a perfect place for generating leads for your business. You might already have been using Instagram, Facebook, and Twitter. The one platform you might not know about is LinkedIn. If you have yet to make the most out of this platform or if you have only dipped your toe into the lead generation opportunities it offers, now is the time to get started with LinkedIn. Chances are that your competitors are already using it, and it's never too late to catch up using advanced LinkedIn lead generation tools to find leads and convert them into customers. So, here's a guide on how to get B2B leads from LinkedIn. How to Create a LinkedIn Lead Generation Strategy? Generating leads on LinkedIn might feel a little overwhelming. But the truth is with little effort and time; you can become an expert in LinkedIn lead generation. For marketers who want to take the next step, here's how to use LinkedIn for lead generation: Optimize Your LinkedIn Page You can expect your website to generate leads if it's properly laid out, there are Smart SEO goals set up, it provides proper information, and its content is useful and up-to-date. Similarly, if your LinkedIn profile is optimized, your customers will buy from you. Now, you must be wondering: how to optimize your LinkedIn profile, so it is compelling enough for lead generation? Start by asking yourself the following questions that will justify what your audience expects from you: What information does your audience expect to see when they first land on your profile?
What phrases or keywords might your audience be looking for to assess your product or service? Use these questions to decide what to put on your profile that can attract your audience to your brand. Make sure to add a catchy headline and a to-the-point summary. Also, do not forget to add the visuals. It is estimated that profiles with visuals are more likely to be viewed as compared to others. So, select high-quality visuals that describe your brand products or services. Post-High-Quality Content on Your LinkedIn Page To make sure that your LinkedIn profile has a great online presence, consider posting high-quality content types. Featuring the content on your page can help you position yourself as a knowledgeable source that establishes credibility with potential clients. When you're known to be a valuable source, you will soon start connecting with like-minded professionals who match perfectly with your ideal customer profile. If you post regularly, your profile will be shown on your leads feed, and the familiarity will make it easier to build further conversations. If you think that your LinkedIn friends are professionals in your sector, the content you upload will expose you to new audiences. When your connections 'Like' a post you've created, it may appear in the feed of a new lead. This lead will see that your shared connection liked your post, attesting to your reliability and authority and maybe motivating them to make a connection request. To ensure that you are creating content your audience might want to see, follow your page analytics that resonates the most with them, and post content accordingly. Examine Your LinkedIn Profile Views Chances are you will get only 1-2 profile visits each day, and you will most likely ignore them. Do not do such a thing. Each profile view is worth a lot as it can be a possible lead you missed. It could also be a valuable lead that reaches out to you. They can be a potential client that matters more than a random name in your cold email list. There's a simple solution to this if you ignore your profile visits. Just select "Who viewed your profile" in your page dashboard, and scroll to the names that showed interest in your profile. Send a message to each one of them, and it's possible that some of them will be your potential leads and be interested in your product or service. Be a Part of Groups You must wonder how to get business leads from LinkedIn. Well, LinkedIn groups are the best way to do such a thing. There are thousands of LinkedIn groups that are relevant to you and your brand. Professionals use such groups to gain insight into their industry and also to engage with other members. The best part - these groups are private by default, so you can expect high-quality lead generation on LinkedIn. You could also create your own group from the start, but it can be a difficult and time-consuming thing to do. The best way to make the most out of such groups is to find the relevant, ready-made one. These are the groupings that contain your buyer persona and are closely related to the industry in which you operate. Small groups limit your lead potential, while large groups cause your message to get lost. It is best to aim for both. To make your opinion known, use the 'Discussion' option at the top of the group's site and post questions, comments, or assertions. Your messaging should be targeted to each group, and you should know who you're writing to so they can make the best judgment about your organization. Make Use of LinkedIn Automation Tools There are a bunch of the best LinkedIn automation tools like Dripify and Octopus CRM that marketers can use to boost a LinkedIn strategy. These tools let you automate your LinkedIn activity and help you generate potential leads for your brand. It enables you to automate message requests, schedule messages, and also track your responses. It saves you a lot of time by automating all your time-consuming activities on LinkedIn while you can focus on other relevant lead generation tasks. With automation tools, you can automate almost anything on LinkedIn in one command center and dashboard. You can develop LinkedIn lead-generation campaigns that might provide remarkable results. Also, you can keep an eye on these campaigns' performance and monitor your total LinkedIn lead generation progress. Nurture Connections Generating leads is only the first step toward having meaningful prospects. You can not expect each lead to be directly impressed by your product pitch; some of them need nurturing too. The first step towards moving a lead to a sales funnel is having a direct conversation with the connection. Send a custom connection request, and follow it up with a catchy InMail that urges them to respond. There's no secret recipe to being a perfect conversation starter, but personalization works best. Try to strike up a conversation about something you both have in common. Expect this process to take some time. Some of the questions you can ask the lead are: What challenges are they facing in their business?
What are long-term and immediate priorities? Finally, when the lead starts asking questions about your product, it's time for you to pitch them and turn the lead into a client. Give and Get Referrals One of the best LinkedIn marketing strategies is to give and get referrals. You should introduce yourself to each new lead as your network of first-degree relationships expands. Share your e-business card, if possible. Making the initial remark breaks the ice, allows for the prospect of collaboration, and lessens the awkwardness of asking them to be directed to someone else later. Look through a recent client's relationships to discover which ones might be helpful to your company. If you are confident enough in your relationship, send them a list of the individuals, and ask if they mind sending a brief introduction of you to each one. The worst-case scenario is that they refuse. A sales team knows how to ask and who to ask for referrals. They can outperform the competition, reach decision-makers in a single call, and develop connections that can lead to sales. Start Using LinkedIn for Lead Generation Now! Leads are like gold to salespeople and marketers. LinkedIn is the best way where you can generate potential leads for brands that can soon be converted into valuable clients. But even though LinkedIn lead generation might be profitable, it takes time to do. It is likely that you spend months without seeing any results if you don't use the proper tactics to target the appropriate audience. You have to be consistent with your lead generation strategy and take time to find valuable LinkedIn connections using the tips given in this guide. Optimize your profile, strike up meaningful conversations, or share high-quality content. Even then, not every prospect will turn into a sale - but you will have a great chance of converting those leads that are interested in buying from you!** As a marketer, you must know about various platforms that are a perfect place for generating leads for your business. You might already have been using Instagram, Facebook, and Twitter. The one platform you might not know about is LinkedIn. The one platform you might not know about is LinkedIn. If you have yet to make the most out of this platform or if you have only dipped your toe into the lead generation opportunities it offers, now is the time to get started with LinkedIn. Chances are that your competitors are already using it, and it's never too late to catch up using advanced LinkedIn lead generation tools to find leads and convert them into customers. So, here's a guide on how to get B2B leads from LinkedIn. LinkedIn lead generation tools LinkedIn lead generation tools How to Create a LinkedIn Lead Generation Strategy? Generating leads on LinkedIn might feel a little overwhelming. But the truth is with little effort and time; you can become an expert in LinkedIn lead generation. Generating leads on LinkedIn might feel a little overwhelming. But the truth is with little effort and time; you can become an expert in LinkedIn lead generation. For marketers who want to take the next step, here's how to use LinkedIn for lead generation: Optimize Your LinkedIn Page You can expect your website to generate leads if it's properly laid out, there are Smart SEO goals set up, it provides proper information, and its content is useful and up-to-date. Smart SEO goals Similarly, if your LinkedIn profile is optimized, your customers will buy from you. Now, you must be wondering: how to optimize your LinkedIn profile, so it is compelling enough for lead generation? how to optimize your LinkedIn profile, so it is compelling enough for lead generation? Start by asking yourself the following questions that will justify what your audience expects from you: What information does your audience expect to see when they first land on your profile? What phrases or keywords might your audience be looking for to assess your product or service? What information does your audience expect to see when they first land on your profile? What information does your audience expect to see when they first land on your profile? What phrases or keywords might your audience be looking for to assess your product or service? What phrases or keywords might your audience be looking for to assess your product or service? Use these questions to decide what to put on your profile that can attract your audience to your brand. Make sure to add a catchy headline and a to-the-point summary. Also, do not forget to add the visuals. It is estimated that profiles with visuals are more likely to be viewed as compared to others. So, select high-quality visuals that describe your brand products or services. Post-High-Quality Content on Your LinkedIn Page To make sure that your LinkedIn profile has a great online presence, consider posting high-quality content types. Featuring the content on your page can help you position yourself as a knowledgeable source that establishes credibility with potential clients. When you're known to be a valuable source, you will soon start connecting with like-minded professionals who match perfectly with your ideal customer profile. If you post regularly, your profile will be shown on your leads feed, and the familiarity will make it easier to build further conversations. If you think that your LinkedIn friends are professionals in your sector, the content you upload will expose you to new audiences. When your connections 'Like' a post you've created, it may appear in the feed of a new lead. This lead will see that your shared connection liked your post, attesting to your reliability and authority and maybe motivating them to make a connection request. To ensure that you are creating content your audience might want to see, follow your page analytics that resonates the most with them, and post content accordingly. Examine Your LinkedIn Profile Views Chances are you will get only 1-2 profile visits each day, and you will most likely ignore them. Do not do such a thing. Each profile view is worth a lot as it can be a possible lead you missed. It could also be a valuable lead that reaches out to you. They can be a potential client that matters more than a random name in your cold email list. There's a simple solution to this if you ignore your profile visits. Just select "Who viewed your profile" in your page dashboard, and scroll to the names that showed interest in your profile. Send a message to each one of them, and it's possible that some of them will be your potential leads and be interested in your product or service. Be a Part of Groups You must wonder how to get business leads from LinkedIn. Well, LinkedIn groups are the best way to do such a thing. There are thousands of LinkedIn groups that are relevant to you and your brand. Professionals use such groups to gain insight into their industry and also to engage with other members. You must wonder how to get business leads from LinkedIn. The best part - these groups are private by default, so you can expect high-quality lead generation on LinkedIn. You could also create your own group from the start, but it can be a difficult and time-consuming thing to do. The best way to make the most out of such groups is to find the relevant, ready-made one. These are the groupings that contain your buyer persona and are closely related to the industry in which you operate. Small groups limit your lead potential, while large groups cause your message to get lost. It is best to aim for both. To make your opinion known, use the 'Discussion' option at the top of the group's site and post questions, comments, or assertions. Your messaging should be targeted to each group, and you should know who you're writing to so they can make the best judgment about your organization. Make Use of LinkedIn Automation Tools There are a bunch of the best LinkedIn automation tools like Dripify and Octopus CRM that marketers can use to boost a LinkedIn strategy. These tools let you automate your LinkedIn activity and help you generate potential leads for your brand. It enables you to automate message requests, schedule messages, and also track your responses. It saves you a lot of time by automating all your time-consuming activities on LinkedIn while you can focus on other relevant lead generation tasks. LinkedIn automation tools With automation tools, you can automate almost anything on LinkedIn in one command center and dashboard. You can develop LinkedIn lead-generation campaigns that might provide remarkable results. Also, you can keep an eye on these campaigns' performance and monitor your total LinkedIn lead generation progress. Nurture Connections Generating leads is only the first step toward having meaningful prospects. You can not expect each lead to be directly impressed by your product pitch; some of them need nurturing too. The first step towards moving a lead to a sales funnel is having a direct conversation with the connection. Send a custom connection request, and follow it up with a catchy InMail that urges them to respond. There's no secret recipe to being a perfect conversation starter, but personalization works best. Try to strike up a conversation about something you both have in common. Expect this process to take some time. Some of the questions you can ask the lead are: What challenges are they facing in their business? What are long-term and immediate priorities? What challenges are they facing in their business? What challenges are they facing in their business? What are long-term and immediate priorities? What are long-term and immediate priorities? Finally, when the lead starts asking questions about your product, it's time for you to pitch them and turn the lead into a client. Give and Get Referrals One of the best LinkedIn marketing strategies is to give and get referrals. You should introduce yourself to each new lead as your network of first-degree relationships expands. Share your e-business card, if possible. Making the initial remark breaks the ice, allows for the prospect of collaboration, and lessens the awkwardness of asking them to be directed to someone else later. Look through a recent client's relationships to discover which ones might be helpful to your company. If you are confident enough in your relationship, send them a list of the individuals, and ask if they mind sending a brief introduction of you to each one. The worst-case scenario is that they refuse. A sales team knows how to ask and who to ask for referrals. They can outperform the competition, reach decision-makers in a single call, and develop connections that can lead to sales. Start Using LinkedIn for Lead Generation Now! Leads are like gold to salespeople and marketers. LinkedIn is the best way where you can generate potential leads for brands that can soon be converted into valuable clients. But even though LinkedIn lead generation might be profitable, it takes time to do. But even though LinkedIn lead generation might be profitable, it takes time to do. It is likely that you spend months without seeing any results if you don't use the proper tactics to target the appropriate audience. You have to be consistent with your lead generation strategy and take time to find valuable LinkedIn connections using the tips given in this guide. Optimize your profile, strike up meaningful conversations, or share high-quality content. Even then, not every prospect will turn into a sale - but you will have a great chance of converting those leads that are interested in buying from you!** Optimize your profile, strike up meaningful conversations, or share high-quality content.

How to Get Business Leads from LinkedIn?

কিভাবে LinkedIn থেকে বিজনেস লিড পাবেন?

Wie erhält man Geschäftskontakte über LinkedIn?

¿Cómo conseguir clientes potenciales comerciales de LinkedIn?

Comment obtenir des prospects sur LinkedIn ?

लिंक्डइन से बिजनेस लीड कैसे प्राप्त करें?

LinkedIn からビジネスリードを取得するにはどうすればいいですか?

LinkedIn에서 비즈니스 리드를 얻는 방법은 무엇입니까?

Como obter leads de negócios no LinkedIn?

Как получить потенциальных клиентов из LinkedIn?

LinkedIn'den İş Potansiyeli Nasıl Alınır?

Làm cách nào để có được khách hàng tiềm năng kinh doanh từ LinkedIn?

如何从 LinkedIn 获取商业线索？

5 Free Data Recovery and Backup Projects to Donate to Via Kivach

কিভাচের মাধ্যমে দান করার জন্য 5টি বিনামূল্যে ডেটা পুনরুদ্ধার এবং ব্যাকআপ প্রকল্প

5 kostenlose Datenwiederherstellungs- und Backup-Projekte, die Sie an Via Kivach spenden können

5 proyectos gratuitos de recuperación y copia de seguridad de datos para donar a Via Kivach

5 projets gratuits de récupération et de sauvegarde de données à faire don à Via Kivach

5 निःशुल्क डेटा रिकवरी और बैकअप प्रोजेक्ट जिन्हें आप वाया किवाच को दान कर सकते हैं

Kivach 経由で寄付できる 5 つの無料データ復旧およびバックアップ プロジェクト

Via Kivach에 기부할 5가지 무료 데이터 복구 및 백업 프로젝트

5 projetos gratuitos de recuperação e backup de dados para doar à Via Kivach

5 бесплатных проектов по восстановлению и резервному копированию данных, которые можно пожертвовать через Кивач

Via Kivach'a Bağış Yapabileceğiniz 5 Ücretsiz Veri Kurtarma ve Yedekleme Projesi

5 dự án sao lưu và phục hồi dữ liệu miễn phí quyên góp cho Via Kivach

向 Via Kivach 捐赠的 5 个免费数据恢复和备份项目

(AI) has emerged as a game-changer in many industries. Predictive AI technology has helped reshape age-old processes, saving organizations time, money, and frustration. A litany of different organizations have leveraged AI solutions to streamline their operations and ultimately drive productivity, changing industries ranging from art to finance. Artificial intelligence While some have bemoaned the rise of AI, wringing their hands over a possible AI takeover, others have recognized the power of machine learning, algorithms, and computer vision to transform their jobs and allow them to get back to the innovating that truly moves the needle towards progress. AI for Boosting Productivity AI has proven to be a boon for productivity within many organizations. By automating repetitive tasks and allowing employees to focus on the parts of their jobs that carry higher value, AI paves the way for more creativity, innovation, and problem-solving. “By analyzing trends in task completion times and quality and assigning specific team members based on past performance and partnerships, AI encourages a culture of recognition and motivation,” says Brianna Van Zanten, Customer Success Manager at InCheq. “When workers' achievements are acknowledged through data-driven insights, it provides a tangible record of their individualistic abilities.” With more workers taking heed of the message of the Great Resignation and seeking work that is personally and professionally rewarding, utilizing AI to create a better work culture can help companies with talent retention and employee satisfaction, not to mention productivity. With actionable insights from performance data, AI can not only make the workplace better for the employees but also significantly improve the overall performance of the organization as a whole. AI for Content Creation With the rise of AI, many in creative fields have feared that predictive AI creations signal the end of human creativity. However, several organizations have learned to use AI not only to help them be more productive but also to give them the space they need to be more creative. “As a podcast host, I can attest to AI's transformative potential for the podcasting industry,” explains Heidi Moore, host of Wine Crush Podcast. “When we integrate AI into existing podcast workflows, it's not merely an additive process — it’s a paradigm shift. The capability of AI to sift through vast datasets allows it to predict and generate engaging topics that resonate deeply with the audience's preferences and current trends. This predictive prowess is instrumental in crafting content strategies that captivate listeners and foster a loyal audience base.” By automating the routines that take away from the creative process, creative entrepreneurs and leaders can focus more on experimenting and enjoying the fun parts of working within a creative space. If they view AI as a collaborative partner in their creative work rather than a hindrance, they may come by a more efficient process. AI for the Supply Chain As we saw laid bare during the pandemic, the efficiency and health of the global supply chain are critical for business. The introduction of AI has allowed those in the supply chain space to optimize traditional processes and explore technology-informed ways of making things run smoothly worldwide. “AI and RFID technology can be pivotal in transforming supply chain operations, particularly in enhancing the efficiency and sustainability of eco-friendly products,” says Joseph Schlossberg, VP of Global Sales at Sourcing Solutions International. “By integrating RFID's real-time tracking capabilities with AI's analytical prowess, we could significantly streamline supply chain processes, ensuring accurate inventory management and reducing unnecessary waste. This synergy is vital for adapting to rapid market changes and managing resources more effectively.” AI use within the supply chain significantly impacts the recent push for sustainability. “By evaluating extensive data on lifecycle impacts, supply chain practices, and product usage, AI facilitates more sustainable decision-making and innovation,” Schlossberg explains. “It helps us improve product design and predicts the environmental impact of materials and processes that we use.” AI in Finance AI also has a significant role to play in an industry like finance, where accuracy is paramount. “As more and more financial advisors embrace advancements in technology, AI continues to play a larger role,” says Aaron Cirksena of MDRN Capital. With the power to analyze vast data sets and uncover valuable insights for investors, AI can unlock new opportunities within the complex world of finance. “We can use AI in a multitude of ways, whether it is used to manage investments, give predictive AI-driven recommendations, or assist advisors in the day-to-day operations of their firms,” Cirksena notes. “Additionally, more advisors are embracing AI for client communication and assisting them with finding new lead sources.” Financial entities that embrace these technologies within the financial space stand to gain an advantage over those competitors who choose not to evolve or even bend with the changing times. As AI becomes more a part of everyday life, more investors and everyday banking consumers will come to expect the value and efficiency that AI can bring, gravitating towards those organizations that can offer it. AI automation has left its fingerprints in nearly every industry. As we head into the future, the potential for AI in areas from healthcare to the arts promises to reshape the way we work, produce, and innovate. Lead image source

Four Use-Cases for AI Automation in 2024

2024 সালে AI অটোমেশনের জন্য চারটি ব্যবহার-কেস

Vier Anwendungsfälle für KI-Automatisierung im Jahr 2024

Cuatro casos de uso para la automatización de la IA en 2024

Quatre cas d'utilisation pour l'automatisation de l'IA en 2024

2024 में एआई स्वचालन के लिए चार उपयोग-मामले

2024 年の AI 自動化の 4 つのユースケース

2024년 AI 자동화를 위한 4가지 사용 사례

Quatro casos de uso para automação de IA em 2024

Четыре варианта использования автоматизации ИИ в 2024 году

2024'te Yapay Zeka Otomasyonu için Dört Kullanım Örneği

Bốn trường hợp sử dụng cho Tự động hóa AI vào năm 2024

2024 年人工智能自动化的四个用例

Virtual voodoo dolls, autonomous avatars & digital doppelgangers will be your behavior-profiling passports in the metaverse: perspective Your metaverse identity, with its , biometric data capture, and behavior profiling, will be central to your everyday life, according to a World Economic Forum (WEF) report. digital ID “Metaverse identity” is a relatively new concept that the WEF and Accenture describe in great detail in a new 48-page entitled “ .” report Metaverse Identity: Defining the Self in a Blended Reality According to the report: “As people spend more time exploring, playing and socializing in digital experiences, as well as to the way they express their personal identity.” a person’s metaverse identity will be central to their day-to-day life But what exactly do the unelected globalists mean by “metaverse identity?” “A person’s metaverse identity will be central to their day-to-day life” METAVERSE IDENTITY: DEFINING THE SELF IN A BLENDED REALITY, WEF, MARCH 2024 Metaverse identity encompasses three components: : including personal, social and role identity, be it through avatars, pseudonyms or other digital expressions Representation : capturing the intricate web of knowledge about individuals generated by metaverse supporting hardware and software Data : be it through driver’s licenses, government-issued IDs, passports, birth certificates, attestations, labels, or usernames and passwords Identification (ID) According to the report, “ .” Metaverse identity broadens ‘identity’ as it is known today and combines it with the digital underpinnings of the internet. It is a multi-layered construct of an individual or entity, including everything from representation to data and identification With these three components, identity in the metaverse “ .” connects and anchors a person to the physical and virtual world Let’s break them down, starting with . representation “The notion of ‘representation’ is not just about pixels and graphics; it’s a reflection of societal values, inclusivity and the human desire for authenticity […] Representational design choices extend to the design of digital entities – from embodied virtual agents to non-embodied virtual assistants” METAVERSE IDENTITY: DEFINING THE SELF IN A BLENDED REALITY, WEF, MARCH 2024 has to do with how you present and express yourself in the , whether it’s a realistic likeness of yourself or an abstract, creative, or artistic version. Representation metaverse The authors say that, “ ,” so there is an element of behavior profiling going on in the representation category, which we will also see in the data capture and identification categories. These expressions may extend to include words, actions, behaviors and mannerisms Representation in the metaverse will also take on a new meaning with the introduction of digital entities that act on your behalf. According to the report, “ […] .” Digital entities may represent humans, objects, systems or abstract concepts, and are capable of varying degrees of interaction, autonomy and behavior within digital experiences They are capable of mimicking human communication and may be used as sales assistants, corporate trainers, social media influencers and more Like non-player characters (NPCs) in videogames, these digital doppelgangers attempt to mimic human behavior. And like a virtual voodoo doll, if something goes wrong with your digital entity, it could spill over into your personal life in the real world. “ ,” the report cautions, adding, “ .” While digital entities can offer innovative interactions and functionalities within the metaverse, they may commit real-world harms The ability to manipulate or create misinformation through digital entities – such as chatbots and photoreal avatars – raises ethical and security concerns Now let’s dig into the category. data “Identity goes beyond ID, like a passport or driver’s license. Metaverse identity includes data points” METAVERSE IDENTITY: DEFINING THE SELF IN A BLENDED REALITY, WEF, MARCH 2024 The category of metaverse identity is aimed at gathering and analyzing data to make inferences about you, and it will be used to predict your behavior, thanks to AI and Machine Learning. data “ ,” the report reads. Paired with artificial intelligence (AI)/machine learning (ML) models that can analyze a person’s interactions, movements and preferences further generates identity “ . Whether these (inferred) data points are capturing a person’s current activities, predicting their next action or future preferences, these data-based breadcrumbs provide information into one’s identity “ .” These attributes may influence the way the virtual environment responds to an individual, and outsiders perceive an individual or entity This aspect of metaverse identity is important to unelected globalists who are obsessed with manipulating human behavior and controlling what people think and do. data “There is a risk that governments could use aggregated inferred data for surveillance, monitoring dissidents, or suppressing certain groups without their active consent” METAVERSE IDENTITY: DEFINING THE SELF IN A BLENDED REALITY, WEF, MARCH 2024 Inferred data, “ ,” according to the report. now aided by AI/ML, can examine seemingly unrelated behaviors, actions and choices to draw meaningful conclusions about a person’s preferences, background and intentions Once you know intent, whether of an individual or an unelected globalist think tank, then it becomes a lot easier to predict what they’ll do or say next, even if they deceptively try to mask their true intentions. And, “ .” While this data is collected to enhance the person’s experience, it could also be analyzed to make inferences as to their real-world identity or preferences and used for targeted advertising or other purposes without their consent Making inferences for “other purposes” without the user’s consent is what defense and intelligence agencies are after in the metaverse. A recent suggests as much with respect to the US Department of Homeland Security (DHS) looking to spy on users in the metaverse, stating, “ .” RAND Corporation report As DHS grapples with emerging challenges by monitoring and analyzing users’ activity in metaverses, it should undertake legal and ethical reviews of what information is collected and how it is managed Now, we shall move on to the category. identification “The concept of identity is contextual, flexible, complex and fluid […] Identity extends into the intricacies of an individual’s behaviors, actions and choices” METAVERSE IDENTITY: DEFINING THE SELF IN A BLENDED REALITY, WEF, MARCH 2024 Perhaps the most confusing part of metaverse identity is the or ID aspect because digital ID and digital identity are terms that are often used interchangeably, including by yours truly, but they are quite different. identification Identification is about identifying, verifying, and authenticating who someone is. Identity on the other hand, ,” according to the report. “consists of layered aspects of cultural heritage, ethnicity, age, professional and social roles, hobbies, gender identification, sexual orientation and much more Now that we’ve taken care of the distinction between the two, your digital ID will be your passport to the metaverse, and like with a passport, there will be certain areas that you will not be able to access. According to the report, “ .” Similar to today’s traditional identification systems – like passports and driver’s licenses – IDs may evolve to include unique avatar designs, new body-based attestations or unique virtual signatures that validate one’s existence and grant access to specific realms or activities To illustrate how digital ID plays into digital identity, the authors say, “ .” Forms of ID – such as passports and government IDs – formalize an individual’s identity; additionally, these can serve as credentials or means of authenticating and verifying individuals across physical and digital spaces “Metaverse identity is integral to future internet interactions” Metaverse Identity: Defining the Self in a Blended Reality, WEF, March 2024 Now that we’ve gone through the three components that make up metaverse identity, what are some of the potential drawbacks? For starters, the report says, “ .” There is a risk that governments could use aggregated inferred data for surveillance, monitoring dissidents or suppressing certain groups without their active consent Your metaverse identity can include your real-time biometrics, such as pupil dilation, heart rate, and brainwaves, so that companies and governments can infer how you are feeling and reacting to their goods, services, or policies. Historian of this same technology falling into the hands of dictators at the 2020 WEF Annual Meeting in Davos. There, he said: Yuval Noah Harari spoke “ . Just imagine North Korea in 20 years where everybody has to wear a biometric bracelet, which constantly monitors your blood pressure, your heart rate, your brain activity 24 hours a day “ .” You listen to a speech on the radio by the ‘Great Leader,’ and they know what you actually feel — you can clap your hands and smile, but if you’re angry, they know you’ll be in the gulag tomorrow morning Having a biometric data capturing device attached to your body that knows what you’ll do before you do it raises serious ethical questions about how the data is collected, where that data goes, and who has access to some of the most intimate details of your life. For the past few years, Meta has been working on , which combines augmented reality with artificial intelligence to create realistic 3D renderings of people, places, and things, including living spaces. Project Aria When Mark Zuckerberg explained his vision for the metaverse at Connect 2021, he highlighted how Project Aria could map a person’s apartment, including everything in it (see video below). https://www.youtube.com/watch?v=Uvufun6xer8&embedable=true Imagine how valuable that information would be to companies — knowing which products you use, which ones they think you’ll want, and how you organize your living space, so they can manufacture extremely personalized ads. What could insurance companies do with that data? How would landlords react? Now, think about how governments, intelligence agencies, and law enforcement would love to get their hands on that type of data. With the data collected from someone wearing a pair of AR glasses, who needs facial recognition, geolocation tracking, or contact tracing when governments and corporations can see what you see, hear what you hear, and know where you and what you are doing in real-time? Of course, these dystopian scenarios need not come to pass, and indeed great efforts are being made to safeguard privacy in the design of these tools and systems — at least for now. The metaverse identity report is replete with cautionary tales and references to building systems that are fair, just, diverse, equitable, inclusive, privacy-preserving, and every other type of virtuous buzzword they think you want to hear. Over time; however, goalposts may shift, emergencies may be declared, and laws and regulations may be circumvented. And what do the authors of the metaverse identity report propose to safeguard the metaverse and its integrity? Their solution is to put “the onus on the global community.” “ .” The metaverse could be fertile ground for powerful manipulative tactics, putting the onus on the global community to establish robust frameworks that not only facilitate the growth of the metaverse but also safeguard its integrity Is “the global community” ever defined in the report? No, but the WEF calls itself the “International Organization for Public-Private Cooperation” and has its Global Innovators, Young Global Leaders, and Global Shapers communities, which might give us a clue as to whom they are referring. The metaverse will no doubt change the way we work and play, leading to exciting cross-disciplinary collaborations, scientific discoveries, and untapped marketplaces. But if the unelected globalists and unaccountable technocrats are in charge of governance, the metaverse will be nothing more than a digital playground for the great reset agenda where your digital identity determines your level of entry and where anything you say or do in the virtual world will come back to haunt you in the physical one and vice versa — there will be no distinction between the two. Once everyone is hooked up to a digital identity while plugged into the metaverse, all that is needed to quash dissent is a simple flick a switch on someone’s digital identity and voila! it’s like that person doesn’t exist anymore. Your metaverse identity, with its virtual voodoo dolls, autonomous avatars, and digital doppelgangers will be your passport in the metaverse, where it will be used to surveil, predict, and mimic your behavior while determining your level of access to information and spaces. But not to worry, your metaverse identity will be essential to your day-to-day life and will be integral to all your future internet interactions. only only This article was originally published by on Tim Hinchliffe The Sociable.

The Metaverse Identity: A Soon-To-Be Cornerstone of Daily Life According to WEF Report

দ্য মেটাভার্স আইডেন্টিটি: ডব্লিউইএফ রিপোর্ট অনুসারে দৈনন্দিন জীবনের একটি শীঘ্রই হতে হবে

Die Metaverse-Identität: Laut WEF-Bericht bald ein Eckpfeiler des täglichen Lebens

La identidad del metaverso: una futura piedra angular de la vida diaria según el informe del WEF

L'identité du métaverse : une pierre angulaire de la vie quotidienne selon le rapport du WEF

मेटावर्स पहचान: WEF रिपोर्ट के अनुसार दैनिक जीवन की जल्द ही आधारशिला बनने वाली है

メタバースのアイデンティティ: WEF レポートによると、まもなく日常生活の基盤となる

메타버스 아이덴티티: WEF 보고서에 따르면 곧 일상 생활의 초석이 될 것입니다.

A identidade do metaverso: uma futura pedra angular da vida diária, de acordo com o relatório do WEF

Согласно отчету ВЭФ, идентичность Метавселенной: скоро станет краеугольным камнем повседневной жизни

Metaevren Kimliği: WEF Raporuna Göre Günlük Yaşamın Yakında Olacak Bir Temel Taşı

Bản sắc Metaverse: Nền tảng sắp có của cuộc sống hàng ngày Theo Báo cáo của WEF

世界经济论坛报告称，元宇宙身份即将成为日常生活的基石

GMX v2 and Its Competitors: The New Wave In the Decentralized Derivatives Market And Its Impact

SINGAPORE, Singapore, April 3rd, 2024/Chainwire/--Skate is backed by leading founders in web3, including EigenLayer, Polygon, Manta, Axelar, Pendle, A41 and Galxe, supporting its vision to make web3 efficient for a modular future by solving application fragmentation
Range Protocol, a unified liquidity provisioning platform, today announced the launch of Skate, the universal application layer that empowers apps to run on thousands of chains with one state. Designed with an intent-centric approach, Skate aims to efficiently address the application fragmentations as a single hub for all code deployment across all chains. Backed by leading web3 players, including EigenLayer, Polygon, Manta, Axelar, Biconomy, Pendle, A41, Vertex, Navi, Galxe, Pontem and more, Skate will deliver its vision with faster finality and universal application scope, paving the way for its mainnet launch within the year. In today’s multi-chain landscape, applications face pressing needs to deploy, adapt and maintain across an increasing number of chains. Skate introduces the concept of a Universal Application Scope, where essential applications are developed collectively and maintained in a shared pool accessible to all chains — regardless of its underlying Virtual Machine environment. With Skate, users and developers are able to efficiently and instantly access thousands of chains by interacting with one application instance. Siddharth Lalwani, Co-Founder and CEO of Range Protocol shared, “The rise of modularity powered innovations such as improved throughput and reduced transaction costs. However, it also came with its own set of challenges, most notably, application fragmentation. Skate introduces the concept of the Universal Application Layer, where essential applications are developed collectively, and maintained in a shared pool accessible to all chains. This ensures foundational needs of builders and users are met efficiently, allowing each chain to focus on creating value-added services and laying the building blocks for a modular future.” Move from Duplication to Innovation: One Skate, One State Skate is the only intent-centric application layer in the ecosystem, enabling applications to run across thousands of chains and different tech stacks at the same time through a single interface. Moving past duplication of deployments, Skate works as a hub for deploying, developing and maintaining a singular version of smart contracts while servicing users across diverse chains. One of the key innovations of Skate is the embedding of interoperability within the application logic, reversing the legacy approach of building apps first and integrating interoperability afterwards. Skate ensures that all applications are created with interoperability as a foundational component, streamlining the development process and removing the necessity to bridge assets, but also significantly enhances the user experience by providing a fluid, interconnected ecosystem where transactions and information flow effortlessly between chains. Aside from the underlying interoperability networks, Skate will be connected to all the blockchains through Fast Finality Network, secured by EigenLayer actively validated service (AVS), to send state attestations from Skate with sufficient trust minimized assumptions. This brings instantaneous cross-chain intent-driven settlements and reduces slippage when making trades while removing unnecessary complexities from an end-user perspective. Securing the Modular Future, Backed by Web3 Pioneers The launch of Skate is supported by buy-ins from leading web3 players, including EigenLayer, Polygon, Manta, Axelar, Biconomy, Pendle, A41, Vertex, Navi, Galxe, Pontem and more. Forged by the same team of financial engineers and web3 developers behind Range Protocol, a unified liquidity provisioning platform covering key DeFi asset classes, the Skate team brings deep cumulative experience from leading companies like Altonomy, Point72, Bybit, Certik and Citigroup. Skate’s launch also follows Range Protocol’s $3.75M seed round last year, led by HashKey Capital and Nomad Capital. In the coming months, Skate aims to introduce Testnet Campaigns to community members with unique incentives. To keep up to date on Skate and its upcoming testnet campaigns, make sure you follow their Twitter: . https://twitter.com/skate_chain About Range Protocol and Skate Underpinned by robust on-chain trading infrastructure, Range Protocol is a unified liquidity provisioning platform. Harnessing advanced expertise and professional strategies, Range Protocol covers key DeFi asset classes through its vault offerings. Combining the best of automated market makers (AMMs) and request-for-quote (RFQ), Range Protocol empowers sound decision-making and optimized strategies for the next generation of investors — with no intermediaries needed. Forged by a team of financial engineers and web3 developers with extensive crypto trading proficiencies, Range Protocol brings deep experience from leading companies like Altonomy, Point72, Bybit, Certik and Citigroup. Its most recent $3.75M seed round was led by HashKey Capital and Nomad Capital. Range now expands into Skate, the universal application layer that empowers apps to run on 1000s of chains with one state. Skate was born with the realization of the limitations of the legacy on-chain trading infrastructure, which deploys repetitive blocks on every new chain. Underpinned by innovation, Skate delivers fast finality across all chains, secured by EigenLayer actively validated service (AVS), and acts as a single hub for all code deployments. Separating assets from pricing, Skate introduces the concept of a Universal Application Scope, where essential applications are developed collectively and maintained in a shared pool accessible to all chains — regardless of its underlying Virtual Machine environment. Solving for dApp/chain-liquidity fragmentation in a modular web3 landscape, Skate ensures foundational needs are met efficiently, allowing each chain to focus on creating unique, value-added services. For more information, please visit: Website: Twitter/X: http://skatechain.org/ https://twitter.com/skate_chain Contact Skate / Range Protocol range@wachsman.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Range Protocol Unveils Skate: An App Layer Powering Apps To Run On All Chains With One State

রেঞ্জ প্রোটোকল স্কেট উন্মোচন করেছে: একটি অ্যাপ্লিকেশান লেয়ার পাওয়ারিং অ্যাপ্লিকেশানগুলি একটি রাজ্যের সাথে সমস্ত চেইনে চালানোর জন্য

Range Protocol stellt Skate vor: eine App-Schicht, die es Apps ermöglicht, auf allen Ketten mit einem Status zu laufen

Range Protocol presenta Skate: una capa de aplicaciones que permite que las aplicaciones se ejecuten en todas las cadenas con un solo estado

Range Protocol dévoile Skate : une couche d'application permettant aux applications de fonctionner sur toutes les chaînes avec un seul état

रेंज प्रोटोकॉल ने स्केट का अनावरण किया: एक ऐप लेयर जो ऐप्स को एक स्टेट के साथ सभी चेन पर चलाने की शक्ति प्रदान करता है

Range Protocol が Skate を発表: 1 つの状態ですべてのチェーン上でアプリを実行できるようにするアプリ層

Range Protocol, 스케이트 공개: 앱이 하나의 상태로 모든 체인에서 실행되도록 지원하는 앱 레이어

Range Protocol revela Skate: uma camada de aplicativo que permite que aplicativos sejam executados em todas as cadeias com um estado

Range Protocol представляет Skate: уровень приложений, позволяющий запускать приложения во всех цепочках с одним состоянием

Range Protokolü Skate'i Tanıtıyor: Uygulamaların Tüm Zincirlerde Tek Durumla Çalıştırılmasına Güç Veren Bir Uygulama Katmanı

Range Protocol ra mắt Skate: Lớp ứng dụng hỗ trợ các ứng dụng chạy trên tất cả các chuỗi với một trạng thái

Range Protocol 推出 Skate：一个应用层，为所有链上运行的应用提供支持

In this era, we’re exploring cyberspace almost as much as the physical world, so we need to pay attention to our security online as well. A lot of potential threats, including malware and exploits, are hiding around our favorite digital spaces and applications. Luckily, there are also a lot of cybersecurity tools available, and we can use some of them for free. Numerous open-source developers have taken the time (and often their own funds) to build these tools for everyone, so, they’d surely appreciate a contribution —in the form of time if you’re another developer or writer, or in the form of donations for everybody else. , an Obyte-based cascading-donation platform, can make your crypto gifts flow across several repositories and reduce any kind of censorship or high fees. Kivach This application only asks for the GitHub profile to send some cryptocurrencies, even if the recipient doesn’t have a wallet yet. Therefore, if you want to donate to an open-source project available on GitHub, you can use Kivach as a decentralized, globally available, unstoppable, as well as faster and cheaper option. In this new episode of our Kivach series, we’ll discover some handy cybersecurity tools available on GitHub to download and use for free. Let’s start! OWASP ModSecurity ModSecurity, born out of Trustwave's SpiderLabs in 2002, is a powerful application firewall (WAF) engine that provides robust protection against a spectrum of web-based attacks. Initially developed as an Apache HTTP Server module, it has since expanded to offer filtering capabilities across diverse platforms like Microsoft IIS and Nginx. Its event-based programming language facilitates HTTP traffic monitoring, logging, and real-time analysis, ensuring heightened security for web applications. Website owners can use it to keep their online spaces safe and secure at every moment, since the app is always on the lookout, analyzing all the traffic. If it spots anything fishy, it jumps into action, blocking those harmful attacks in real time and keeping a record of everything that happens. In other words, is like a digital guardian for websites, protecting them from harmful attacks that could otherwise wreak havoc. this software In February 2024, Trustwave the custodianship of the software to the Open Worldwide Application Security Project (OWASP) —an NGO dedicated to improving cybersecurity through open-source development. This way, ModSecurity is now fully in the hands of its community. It’s possible to contribute to its development via GitHub or donate through its website with fiat currencies. To send them some crypto coins, you can use Kivach, where they appear as . entrusted spiderlabs/owasp-modsecurity-crs Qubes OS Have you ever thought that your commercial operating system (OS), like Windows or MacOS, couldn’t be entirely secure or private by itself? The team of Qubes certainly did it, and that’s why they launched this security-focused OS in 2012, led by Marek Marczykowski-Górecki. Over the years, the project has garnered support from a wide range of users, including journalists, activists, whistleblowers, researchers, and renowned security experts like Edward Snowden and Micah Lee. Unlike traditional operating systems, relies on a concept called compartmentalization, where different parts of the system are isolated from each other like rooms in a building. Qubes OS This isolation helps prevent cyberattacks by limiting the damage that can be caused if one part of the system is compromised. Other key features of Qubes OS include its support for multiple operating systems simultaneously, disposable virtual machines that self-destruct when shut down, a specialized proxy, device isolation for secure handling of network cards and USB controllers, and integration with Whonix for anonymous browsing using Tor. Funding for Qubes OS primarily comes from community support and donations. This ensures that the software remains free, secure, and open-source. Besides credit cards or PayPal, they accept Bitcoin, Ethereum, and Monero. You can also donate to them . via Kivach Nikto Launched in 2001 by Chris Sullo, Nikto is a popular open-source web server scanner. among other tasks. Its comprehensive testing extends to scrutinizing server configurations, detecting the presence of multiple index files, and endeavoring to identify installed web servers and software. It works to conduct thorough examinations of web servers, scrutinizing over 6,700 potentially perilous files/programs and identifying outdated versions of over 1,250 servers, In simpler terms, is to carefully examine every nook and cranny of these web servers to uncover any potential risks or vulnerabilities. It's like scanning the entire house to make sure there are no unlocked doors or windows that hackers could exploit. However, it's worth noting that not every finding uncovered by Nikto is cause for alarm. Some checks serve to provide informative insights, alerting users to elements that might not necessarily pose a security threat but could still benefit from attention. Nikto's job The project is currently maintained by Sullo and David Lodge, besides a community of volunteers on GitHub. Sullo accepts donations via Patreon, which could be kind of expensive and non-private for contributors —not to mention the fees taken from Sullo by the platform. You can donate some coins via Kivach instead, where the project appears as . sullo/nikto Sanesecurity (& ClamAV) Sanesecurity is an organization that has been actively involved in enhancing the effectiveness of ClamAV, a free and open-source antivirus toolkit (owned by Cisco). Since its inception in 2006, particularly concerning macro malware, JavaScript malware, phishing attempts, spam, and other forms of emailed ransomware. Sanesecurity has been providing with hourly updated malware signatures aimed at improving its detection capabilities, ClamAV These signatures are unique identifiers or characteristics that help the antivirus software recognize and detect specific instances of malware. They’re meticulously crafted by the dedicated team at , are of professional quality, and undergo regular updates throughout the day to ensure their relevance and efficacy. This way, Sanesecurity plays a pivotal role in enriching ClamAV's native capabilities. Sanesecurity Unlike commercial entities, Sanesecurity operates as a community-driven initiative, with the sole objective of enhancing ClamAV's functionality (often criticized without this kind of add-on) and bolstering web community security. The team behind Sanesecurity is comprised of volunteers who contribute their time and expertise to produce and maintain these signatures in their spare time. To sustain their efforts, Sanesecurity relies on donations from users who benefit from their signatures. Donations can be made in fiat money through PayPal or credit card, with options for one-time contributions or monthly/yearly subscriptions. On the other hand, Kivach is a decentralized and inexpensive option to send them funds. They appear there as . Sanesecurity/bad3cx x64dbg This one is aimed at programmers and researchers, but it’s equally useful and free in the realm of cybersecurity. In case you don’t know, a debugger is a software tool used by developers and analysts to examine and manipulate the behavior of computer programs during execution. It allows users to step through the code line by line, inspect variables and memory contents, set breakpoints to pause execution at specific points and analyze program state to identify bugs or understand how the program operates. is an open-source binary debugger specifically designed for Windows operating systems. It’s tailored for malware analysis and reverse engineering tasks, particularly for analyzing executable files for which the source code isn’t available. With an intuitive and user-friendly interface, x64dbg offers a comprehensive set of features, including full-featured debugging of both DLL and EXE files, a plugin system, disassembly of code, memory mapping, symbol and thread views, and a graph view to visualize program flow. x64dbg As an open-source project under active development, x64dbg encourages community contributions and donations through GitHub Sponsors. Of course, it’s also available on Kivach as . x64dbg/x64dbg Tell them you donated! Likely, the recipients of your donations via Kivach don’t know about it until you tell them, so don’t forget to communicate with them. with a simple attestation in the same app. Then, they should be able to receive and transfer any cryptocurrency sent in the name of that profile. To claim a donation on this platform, they’ll need to download the and verify their GitHub profile Obyte wallet If you want to discover other interesting projects to use for free and optionally donate to, please check our previous chapters in this series. Kivach Donations: 5 GitHub projects it has helped so far How You Can Support Key Open-Source Initiatives With Kivach 5 Open-source Projects You Can Support With Kivach — and Cryptocurrencies 5 Open-source Projects You Can Support with Kivach and Cryptos, Episode III 5 Open-Source Projects You Can Donate to via Kivach, Episode IV: Privacy Tools 5 Open-source Blogging and Writing Tools to Donate to via Kivach (Ep V) 5 Open-Source Tools You Can Donate to via Kivach, Episode VI: Decentralized Services 5 Open-Source Projects to Donate via Kivach, Episode VII: Games to Play for Free! Featured Vector Image by Freepik

5 Cybersecurity Tools to Use for Free and Donate to Via Kivach

বিনামূল্যে ব্যবহার করার জন্য 5টি সাইবার নিরাপত্তা সরঞ্জাম এবং কিভাচের মাধ্যমে দান করুন৷

5 Cybersicherheitstools, die Sie kostenlos nutzen und an Via Kivach spenden können

Cinco herramientas de ciberseguridad para usar de forma gratuita y donar a Via Kivach

5 outils de cybersécurité à utiliser gratuitement et à faire un don à Via Kivach

5 साइबर सुरक्षा उपकरण जिन्हें आप निःशुल्क उपयोग कर सकते हैं और वाया किवाच को दान कर सकते हैं

無料で使用でき、Kivach に寄付できる 5 つのサイバーセキュリティ ツール

무료로 사용하고 Via Kivach에 기부할 수 있는 5가지 사이버 보안 도구

5 ferramentas de segurança cibernética para usar gratuitamente e doar para Via Kivach

5 инструментов кибербезопасности, которые можно использовать бесплатно и делать пожертвования через Kivach

Ücretsiz Olarak Kullanabileceğiniz ve Via Kivach'a Bağış Yapabileceğiniz 5 Siber Güvenlik Aracı

5 công cụ an ninh mạng có thể sử dụng miễn phí và quyên góp cho Via Kivach

5 个可免费使用并捐赠给 Via Kivach 的网络安全工具

Ethereum Dencun Upgrade: Everything You Need to Know

USA v. Samuel Bankman-Fried Court Filing, retrieved on March 15, 2024 is part of . You can jump to any part in this filing . This part is 27 of 33. HackerNoon’s Legal PDF Series here IV. The Defendant’s History and Characteristics Do Not Warrant a Significant Variance Bankman-Fried’s personal history and characteristics do not warrant a variance from what should be a lengthy sentence of incarceration. Unlike many criminals who come before the Court who began life with almost nothing, Bankman-Fried began with and continued throughout his life to enjoy substantial advantages over others in society. (See Def Mem. at 39-43 (summarizing Bankman-Fried’s comfortable upbringing and education at MIT)). After getting a sought-after job at an “elite quantitative trading firm” earning hundreds of thousands of dollars (Def. Mem. at 43), Bankman-Fried started two companies that could have been successful without fraud. Indeed, as he emphasized during trial, FTX earned around a billion dollars a year in transaction fees by users. But the defendant chose to abandon honest work to pursue profit and influence through crime, and he used the proceeds of those crimes to enjoy his own lifestyle of affluence. The fact that he chose to engage in a massive fraud is an aggravating factor, not a mitigating one. Urging leniency, the defendant has submitted several letters from friends and family attesting to his character. From those, he argues that the evidence at trial did not show the “real Sam.” The fact that the version of the defendant presented at trial is unrecognizable to his family and friends does not mean that the defendant did not willfully engage in enormously damaging conduct. Humans are complicated, and the capacity of one person to be generous while also committing heinous crimes is almost unexplainable. Famously, Madoff was described by several people as a “generous person” who paid for employees’ honeymoons, purchased a machine to read to a blind man, arranged for medical care of an employee’s mother after a stroke, and assisted another employee in her efforts to adopt a child. See United States v. Bonventre, No. 10 Cr. 228 (LTS) (S.D.N.Y.) (Trial Tr. 2649, 2850, 7550, 9501, 10198). This is all to say that the defendant could be honest with his family while he lied to customers and investors; he could behave in a selfless manner with friends while being motivated by influence, power, fame, and wealth in his business. The defendant’s father, brother, and psychiatrist all submitted letters suggesting that Bankman-Fried is not motivated by greed. (Def. Mem. at 50-52). While that may be their experience, the trial record demonstrates otherwise: the defendant engaged in a myriad of fraudulent conduct to maximize his business’s earnings and his own wealth. He may have claimed that he intended to give all his money away, but the evidence at trial demonstrated that the defendant lived in an expensive penthouse apartment, owned other properties, bought a home for his parents, gifted his parents millions of dollars, named the Miami Heat’s sports arena after FTX, paid for a Superbowl advertisement, paid millions of dollars to K5 to be connected to celebrities and politicians, made risky venture investments, flew around in a private jet, and made millions of dollars in political donations. The defendant makes a point in his sentencing submission to note his charitable giving. (Def. Mem. at 46-50). But that was charity with other people’s money. The defendant’s contributions to the FTX Foundation, which he now highlights, and his brother’s pandemic prevention non-profit, Guarding Against Pandemics, were paid for with customers’ funds routed through Alameda. (GX-1035, 1040). Even if that were not the case, prior charitable works, however commendable and extensive, by professionally successful defendants rarely, if ever, should be considered a materially mitigating factor because, as courts have previously recognized, it is not extraordinary for such individuals to be involved in charities. See, e.g., United States v. Barbera, No. 02 Cr. 1268 (RWS), 2005 WL 2709112, at *12-13 (S.D.N.Y. Oct. 21, 2005); United States v. Fishman, 631 F. Supp. 2d 399, 403 (S.D.N.Y. 2009) (a defendant’s “good name and good works” should not serve as “the human shield he raises to seek immunity or dramatic mitigation of punishment when he is caught”). Indeed, many defendants do not have the resources—in time, money, or social standing—to perform such deeds, and so the law is reticent to show leniency to the few defendants who are fortunate enough to have such options. See, e.g., United States v. Vrdolyak, 593 F.3d 676, 682 (7th Cir. 2010) (“Wealthy people commonly make gifts to charity. They are to be commended for doing so but should not be allowed to treat charity as a get-out-ofjail card.”); United States v. Ali, 508 F.3d 136, 149 & n.17 (3d Cir. 2007) (charitable service is “evaluated with reference to the offender’s wealth and status in life” because defendants “who enjoy sufficient income and community status . . . have the opportunities to engage in charitable and benevolent activities.” (citations and quotation marks omitted)); United States v. McClatchey, 316 F.3d 1122, 1135 (10th Cir. 2003) (“[E]xcellent character references are not out of the ordinary for an executive who commits white-collar crime; one would be surprised to see a person rise to an elevated position in business if people did not think highly of him or her.”). Bankman-Fried should not be credited with charitable giving when it was done with fraud proceeds and its benefits pale in comparison to the damage done by his fraud. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 15, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener

Charity or Fraud? Uncover the Truth About Sam Bankman-Fried’s Criminal Conduct

How to secure your apps with SSL certificates for custom domains If you’re building an application that needs to go toward production, you’ll undoubtedly need to serve it up securely with SSL. What that entails varies from provider to provider, and you’ll encounter differing levels of complexity (and cost) in getting it all setup. Fortunately, if you’re using Heroku to run your application, this is pretty straightforward. Recently, I've been exploring Heroku again, and in this article, I'll delve into what you'll require to enable SSL on the platform and explore the reasons behind choosing certain features over others. What does Heroku provide for SSL right out of the box? Heroku focuses on ease of use, and I love that. Whether it’s the one-click deploy straight from my GitHub repo or the plugin ecosystem that lets me drop all kinds of great things into my app from the command line, I always appreciate the out-of-the-box options I have at my fingertips. Likewise, SSL on Heroku provides some great default options. Heroku offers two main ways of working with SSL certificates on their platform: Automated Certificate Management (ACM) Heroku SSL For most apps, ACM will give you a really simple, automated certificate management experience—that is the name, after all. But there may be times when you’ll need the more robust Heroku SSL option. We’ll take a look at ACM first. Then, we’ll investigate why you might need Heroku SSL and how you’d use it. Automated Certificate Management and how it works ACM is built on top of —a free, automated certificate authority, run by a nonprofit organization committed to enhancing the security of the web in general. While organizations can sponsor the Let’s Encrypt project, there is no cost for using Let’s Encrypt-issued SSL certificates. Because the certificates are free, Heroku provides Automated Certificate Management with any of their basic plans at no additional charge. Let’s Encrypt While Let’s Encrypt automates the process of requesting and issuing certificates, ACM automates the process of installing those certificates on the relevant apps for the domains they’re issued for. Other methods exist for automated management of certificates, but they’re paid add-ons, so I won’t be looking at them here. Instead, let’s show you how to add ACM to a demo app I’ve already deployed to Heroku. To be clear, there isn’t a whole lot you need to configure for ACM. In fact, if you haven’t added a custom domain to your app, the Heroku-generated URL (on the herokuapp.com domain) for your app already has SSL. Step 1: Enable ACM Your first step is to turn on ACM. The easiest way to do this for any deployed app is with . Assuming you’re logged into the Heroku CLI Heroku via the CLI, all you need to do is run a single command to turn on ACM: $ heroku certs:auto:enable
Enabling Automatic Certificate Management... starting.
See status with heroku certs:auto or wait until active with
heroku certs:auto --wait=== Your certificate will now be managed by Heroku.
Check the status by running heroku certs:auto. If you enable ACM setting up a custom domain on your app, you’ll get a message like this: before $ heroku certs:auto
=== Automatic Certificate Management is enabled on pure-brushlands-82324
=== Add a custom domain to your app by running: heroku domains:add <yourdomain.com> Step 2: Add a custom domain At this point, you simply need to do what the Heroku CLI says and add a domain to your app. You can do this in the CLI too. $ heroku domains:add really-cool-stuff.app
Configure your app's DNS provider to point to the DNS Target lively-basin-0v9xkh99iaz1yc0xldmakla5.herokudns.com.
    For help, see https://devcenter.heroku.com/articles/custom-domains

The domain really-cool-stuff.app has been enqueued for addition
Run heroku domains:wait 'really-cool-stuff.app' to wait for completion
Adding really-cool-stuff.app to ⬢ pure-brushlands-82324... done Step 3: Configure DNS At this point, you need to update your DNS records on your domain to point to the DNS target provided by Heroku. The are detailed and clear. Typically, this means adding a CNAME record that directs to the new location provided by Heroku. instructions in the help documentation it links to Once you have your DNS configured properly, you can check on the status of your certificates. You should see output like this: $ heroku certs:auto
=== Automatic Certificate Management is enabled on pure-brushlands-82324

Certificate details:
Common Name(s): really-cool-stuff.app
Domain(s):      fe6ab605-91f9-4261-974c-ee85c043dbf7
Expires At:     2024-05-27 17:08 UTC
Issuer:         /CN=R3/O=Let's Encrypt/C=US
Starts At:      2024-02-27 17:09 UTC
Subject:        /CN=really-cool-stuff.app
SSL certificate is verified by a root authority.

Domain                       Status       Last Updated
───────────────────────────  ───────────  ────────────
really-cool-stuff.app        Cert issued  4 minutes If you’re like me and you made a mistake when setting up the DNS records 🤦🏻, then you might see some warnings on the app dashboard in the web interface like this: Or this one: Fortunately, ACM repeats its validation attempts regularly for an entire hour. So, I was able to fix my issues quickly. I got to a green status pretty easily. If you’ve ever had to set up SSL on another platform before, you’re probably painfully aware that it usually isn’t this simple (and that’s putting it mildly). That said, Let’s Encrypt has some limitations that may require you to use the more robust offering: Heroku SSL. Heroku SSL and why you might want it While ACM gives you a really simple experience, with that simplicity also comes somewhat limited functionality. Let’s consider two examples where ACM might not meet your unique needs. Example 1: When you need wildcard certificates Let’s Encrypt is restricted to providing certificates on only one single domain name zone at a time. Sometimes, you might actually want to have a wildcard certificate; this is the type of functionality you might need if you have a multi-tenant application that switches between tenants at the subdomain level. ACM doesn’t support this. But Heroku SSL does. common name Example 2: When you need Organizational or Extended Validation (OV/EV) Let’s Encrypt also doesn’t guarantee that you are who you say you are. What I mean is that the type of certificate issued by Let’s Encrypt only attests that the entity requesting the certificate also controls the domain name that it’s securing. There are some scenarios when you need to ensure more than just that your domain traffic is encrypted in transit. You might need to verify that your application is actually associated with your company. In this case, you’d want to purchase an SSL certificate with either Organizational Validation (OV) or Extended Validation (EV). Those usually require checks that can’t be simply automated by a product like Let’s Encrypt. really Using Heroku SSL In the above cases, you’ll need to manually upload your own certificate. As usual, this process has been beautifully simplified by Heroku (but it’s still more work than just telling the platform to start issuing certificates with ACM). To add or update the certificate for your Heroku app, and to use a certificate provide, you need to run a single command: you $ heroku certs:add server.crt server.key
Adding SSL to example... done
exampleapp now served by exemplary-sushi-4gr7rb6h8djkvo9j5zf16mfp.herokudns.com.
Certificate details:
Expires At: 2022-08-18 21:53:18 GMT
Issuer: C=US; ST=CA; L=SF; O=Heroku; CN=www.example.com
Starts At: 2021-08-18 21:53:18 GMT
... Ok, so it’s not much more work than using ACM. that Note that you can’t add multiple intermediate certificates to make a valid chain toward whatever root certificate has signed your new certificate. However, you can into one file so they can be uploaded as a single file. merge your intermediate certificates After uploading your certificate through Heroku SSL, you’ll be able to see your application protected by the SSL certificate you provided on your own. While Heroku SSL still keeps things pretty simple, keep in mind that You’ll also need knowledge of how to work with certificates. While these might not be deal-breakers for you, they’re important points to remember—especially when you consider that ACM takes care of automatic renewals of SSL certificates. With ACM, the simple commands to set it up really are “set it and forget it.” That said, Heroku designed their entire certificate management system in such a way that even if you need the most advanced options, it’s still quite simple and manageable, even if you don’t have extensive certificate experience. you’re responsible for keeping your certificates up to date . Conclusion While there is more you can do with SSL on Heroku, both the ACM and Heroku SSL options can provide the functionality to fit most use cases. Heroku’s also provides enough detail to point you in the right direction. If you haven’t secured your app with SSL (shame on you!), then the simplicity of ACM should remove any excuse you have to secure your site properly. If your use case is more complicated, then Heroku SSL should give you what you need. documentation in this space

How to Secure your Heroku Apps with SSL Certificates for Custom Domains

This paper is available on arxiv under CC 4.0 license. Authors: (1) Md Masud Rana, Department of Mathematics, University of Kentucky; (2) Duc Duy Nguyen, Department of Mathematics, University of Kentucky & ducnguyen@uky.edu. Table of Links Abstract & Introduction Datasets and Results Methods Conclusion, Data and Software Availability, Competing interests, Acknowledgments & References 2 Datasets and Results In this section, we perform validation and evaluation of our proposed models on several benchmark datasets. We develop two types of GGL-PPI models: GGL-PPI1 and GGL-PPI2. The first model, GGL-PPI1, is built solely on geometric graph features discussed in Section 3. On the other hand, GGL-PPI2 incorporates both geometric graph features and auxiliary features, as detailed by Wang et al. [41]. The electrostatic potential calculations for the auxiliary components are conducted using the MIBPB software [42]. 2.1 Validation To validate our models, we primarily consider the AB-Bind dataset [25], SKEMPI 1.0 dataset [23], and SKEMPI 2.0 dataset [24]. We employ a rigorous evaluation methodology by conducting a 10-times 10-fold cross-validation (CV) on each datasets. The mean Pearson correlation coefficient (R ) and root-mean-square error (RMSE) serve as our evaluation metrics. p In comparing the CV performance of our proposed models with other existing methods, we specifically assess TopNetTree [41], Hom-ML-V2 [43], and Hom-ML-V1 [43]. Both TopNetTree and Hom-ML-V2 incorporate auxiliary features in conjunction with their topology-based and Hom-complex-based features, respectively. On the other hand, Hom-ML-V1 solely relies on Hom-complex-based features without utilizing any auxiliary features. Validation on AB-Bind S645 Data Set The AB-Bind dataset contains 1,101 mutational data points for 32 antibody-antigen complexes, providing experimentally determined binding affinity changes upon mutations. Pires et al. curated a subset known as AB-Bind S645 [44], consisting of 645 single-point mutations observed in 29 antibody-antigen complexes. The dataset comprises a mix of stabilizing (20%) and destabilizing (80%) mutations. Additionally, the dataset includes 27 non-binders that do not show any binding within the assay’s sensitivity range. For these non-binders, the binding free energy changes have been uniformly set to a value of 8 kcal/mol. It is crucial to consider these non-binders as outliers during model development and evaluation to ensure model accuracy and robustness. Our GGL-PPI2 achieved an Rp of 0.58 on the AB-Bind S645 dataset, as shown in Figure 2a. The comparison results in Table 1 indicate that our model tied for second place with Hom-ML-V2 43, while TopNetTree [41] claimed the top position. However, when we exclude the 27 nonbinders from the dataset, our model outperforms all other existing models. Specifically, the Rp value increases to 0.74 from 0.58 after removing the nonbinders (Figure 2b). Furthermore, GGL-PI1, our purely geometric graph-based features model, demonstrated competitive performance with an Rp of 0.57 on the AB-Bind S645 dataset. Intriguingly, when excluding the nonbinders, GGL-PPI1 surpassed all other models with an improved Rp of 0.73. These performances reveal that our multiscale weighted colored geometric graphs can effectively characterize the wide range of interactions in biomolecular complexes. Validation on SKEMPI 1.0 S1131 Data Set The SKEMPI 1.0 dataset consists of a collection of 3,047 mutations of 158 complexes obtained from literature sources, where the complexes have experimentally determined structures [23]. The dataset includes both single-point mutations and multi-point mutations. Specifically, there are 2,317 entries in the dataset that represent single-point mutations, which are collectively known as the SKEMPI S2317 set. Additionally, a subset of 1,131 non-redundant interface single-point mutations has been selected from the SKEMPI S2317 set and labeled as the SKEMPI S1131 set [45]. This subset focuses on studying the impact of single-point mutations on protein-protein interactions. Table 1: Performance comparison of different methods in terms of Pearson correlation coefficients (Rp) for the AB-Bind (S645) dataset. Figure 2c shows that our model GGL-PPI2 achieves an Rp of 0.873 and an RMSE of 1.21 kcal/mol in 10-fold CV on the S1131 dataset. Table 2 presents the performance comparison of various methods on the S1131 dataset, including our proposed models, GGL-PPI1 and GGL-PPI2. Among them, our model, GGL-PPI2, achieved the highest performance, underscoring its superiority in predicting binding affinity changes due to mutation. Notably, even without auxiliary features, our GGL-PPI1 outperformed both TopNetTree and Hom-ML-V2 methods that do leverage auxiliary features. This again highlights the efficacy of our geometric graph-based molecular representation. Table 2: Performance comparison of different methods in terms of Pearson correlation coefficients (Rp) for the single-point mutations in the SKEMPI 1.0 (S1131) dataset. Validation on SKEMPI 2.0 S4169 and S8338 Data Sets The SKEMPI 2.0 dataset is an updated and expanded version of the original SKEMPI dataset, incorporating new mutations collected from various sources [24]. Released in 2018, it significantly increased in size, now containing a total of 7,085 entries, including both single-point and multi-point mutations. The data was obtained by merging several databases, including SKEMPI 1.0 [23], AB-Bind [25], PROXiMATE27, and dbMPIKT46. Additionally, new data from the literature were manually curated and added to the dataset. The mutations cover a wide range of protein complexes, such as protease-inhibitor, antibody-antigen, and TRCpMHC complexes. Among the mutations, approximately 3,000 are single-point alanine mutations, 2,000 are single-point non-alanine mutations, and another 2,000 involve multiple mutations. Notably, the authors of the mCSM-PPI2 [8] method filtered the single-point mutations, yielding S4169 set, comprising 4,169 variants in 139 different complexes/ The S8338 set, derived from S4169, represents hypothetical reverse mutation energy changes with negative values. This comprehensive dataset serves as a valuable resource for studying protein interactions and their thermodynamic properties. Perforamnce-wise, Our GGL-PPI2 model posts an Rp of 0.81 with an RMSE of 1.03 kcal/mol for the S4169 dataset as shown in Figure 2d, outstripping all existing models (Table 3). It is noteworthy that our GGL-PPI1 model, which solely relies on geometric graph-based features, demonstrated comparable performance to GGL-PPI2, outperforming TopNetTree and mCSM-PPI2 with an Rp of 0.80 and an RMSE of 1.06 kcal/mol. In the case of the S8338 dataset, we applied a stratified cross-validation approach similar to mCSM-PPI2. We ensured that hypothetical reverse mutations were consistently placed either in the training or test sets during the dataset splits, maintaining their relationship to the corresponding original mutations intact throughout the cross-validation process. GGL-PPI2 achieved an Rp of 0.85 with an RMSE of 1.07 kcal/mol as depicted in Figure 2e, and GGL-PPI1 closely followed, attaining an Rp of 0.84 with the same RMSE value. As Table 3 attests, our GGL-PPI2 is on par with TopNetTree and outperforms mCSM-PPI2 on the S8338 dataset. Table 3: Performance comparison of different methods in terms of Pearson correlation coefficients (Rp) for the single-point mutations in the SKEMPI 2.0 (S4169 and S8338) dataset. 2.2 Evaluation To evaluate our proposed model for predicting binding free energy (BFE) changes of protein-protein interactions, we consider two datasets sourced from the ProTherm database [22]. The first dataset, carefully selected by Pucci et al. [36], named S[sym] dataset. This data assembles 684 mutations from the ProTherm, comprising 342 direct mutations and their corresponding reverse mutations, resulting in a balanced dataset. The dataset specifically focuses on mutations in fifteen protein chains with solved 3D structures, ensuring highresolution data with a resolution of at least 2.5˚A. By providing experimentally measured ∆∆G values and a balanced representation of stabilizing and destabilizing mutations, the S[sym] dataset serves as a valuable resource for evaluating prediction biases in the context of predicting mutation-induced binding affinity changes. To address the issue of data leakage and enhance the generalization capability of our method, we employed the Q1744 dataset [47]. Quan et al. [48] compiled the Q3421 dataset from ProTherm, consisting of 3421 single-point mutations across 150 proteins with available PDB structures. However, the presence of homologous proteins in both the training and test set can lead to interdependent effects of mutations, compromising the model’s performance. To mitigate this, Li et al. [47] created the Q1744 dataset, derived by excluding overlapping data points and refining protein-level homology between Q3421 and S[sym] datasets, resulting in 1744 distinct mutations. Furthermore, the Q3488 dataset was created by augmenting reverse mutations in the Q1744 set. We utilized the Q3488 dataset as our training set, thereby enhancing our ∆∆G predictor’s capability to accurately predict BFE changes in PPIs. We conduct an evaluation of our model on the blind test set S[sym], with a distinct focus on both direct and reverse mutations. To assess the performance, we utilize the Pearson correlation coefficient and root-mean-square error as our primary metrics. Additionally, to discern any prediction bias, we incorporated two statistical measures: Rpdir−rev and δ. The former calculates the Pearson correlation between predictions for direct and reverse mutations, while the latter represents the sum of predicted ∆∆G values for both types of mutations. The hypothesis is that an unbiased predictor would yield Rpdir−rev = −1 and an average δ ( ¯δ) of 0 kcal/mol. Our main focus is to highlight the effectiveness of our model, GGL-PPI2, particularly emphasizing its robust geometric graph-based molecular featurization. GGL-PPI2 has demonstrated exceptional prediction accuracy, maintaining consistency for both direct and reverse mutations. As depicted in Figure 3a and 3b, our model achieves consistent Rp values of 0.57 and an RMSE of 1.28 kcal/mol, indicating its efficiency against overfitting to direct mutations. Additionally, the analysis reveals that a significant proportion of mutations fall within a prediction error of 0.5 kcal/mol and 1.0 kcal/mol, with 34.6% and 65.8% for direct mutations and 35.1% and 66.0% for reverse mutations, as depicted in Figure 3d and 3e. Furthermore, Figure 3c demonstrates that GGL-PPI2 effectively addresses prediction bias by achieving a nearly perfect Rpdir−rev value of -0.999 and an extremely low average ¯δ of 0.006 kcal/mol. Finally, the distribution plot in Figure 3f illustrates that 99.4% of mutations exhibit a prediction bias under 0.05 kcal/mol. In Table 4, we present the prediction results of our models and conduct a comprehensive comparison with other ∆∆G predictors. We observe that our GGL-PPI2 model outperforms ThermoNet [47], which was also trained on the homologyreduced set Q3488, across all evaluation measures. It outperforms ThermoNet by 21.3% for direct mutations and 18.7% for reverse mutations. Furthermore, the GGL-PPI1 model, which only uses geometric graph-based features, also performs better than ThermoNet in both direct and reverse prediction tasks. This further emphasizes the effectiveness of our geometric-graph approach. For a broader comparison against other ∆∆G predictors, we introduce the GGL-PPI2∗ model, trained on the Q6428 set constructed before the homology reduction of the set Q3421 [47]. As illustrated in Table 4, GGL-PPI2∗ excels over other methods in reverse mutation predictions. It is noteworthy that while some methods surpass GGL-PPI2∗ for direct mutations, they frequently exhibit significant bias towards reverse mutations. This paper is available on under CC 4.0 license. Arxiv

Our Datasets and Results From Our Study: GGL-PPI Models

আমাদের গবেষণা থেকে আমাদের ডেটাসেট এবং ফলাফল: GGL-PPI মডেল

Unsere Datensätze und Ergebnisse unserer Studie: GGL-PPI-Modelle

Nuestros conjuntos de datos y resultados de nuestro estudio: modelos GGL-PPI

Nos ensembles de données et résultats de notre étude : modèles GGL-PPI

हमारे डेटासेट और हमारे अध्ययन के परिणाम: जीजीएल-पीपीआई मॉडल

私たちのデータセットと研究結果: GGL-PPI モデル

연구 데이터 세트 및 결과: GGL-PPI 모델

Nossos conjuntos de dados e resultados de nosso estudo: modelos GGL-PPI

Наши наборы данных и результаты нашего исследования: модели GGL-PPI

Veri Setlerimiz ve Çalışmamızın Sonuçları: GGL-PPI Modelleri

Bộ dữ liệu và kết quả từ nghiên cứu của chúng tôi: Mô hình GGL-PPI

我们的数据集和研究结果：GGL-PPI 模型

Comprehensive Tutorials On How To Build And Deploy Decentralised Application On Vara Network

The digital asset industry is not one that is stagnant. Beyond the perennial excitement induced among enthusiasts and users due to the volatile price movements of the assets, the industry is no stranger to eventful occurrences. Most recently, the US SEC (Securities and Exchange Commission) approved the issuance of Bitcoin ETFs (Exchange Traded Funds) for a handful of prominent asset management and financial services companies. The move pushes the crypto industry towards institutionalization, with multiple jurisdictions worldwide already regulating or working on implementing regulations for all kinds of digital assets beyond Bitcoin. As the industry matures and the innovation evoked by the asset class attracts retail and institutional investors alike, its institutionalization is the only logical step ahead. The Good Comes with the Bad – Still, the Industry Grows Beyond those desired happenings, the industry has also been prone to setbacks. For example, the recent Binance debacle saw the company shelling out over due to its negligence in reporting and preventing transactions serving money laundering. Another example is the infamous that led to collective losses in the billions for crypto users due to the blatant mismanagement of funds. $4 billion in fines FTX saga Regulations will ensure such failures do not occur, keeping digital asset investors and users safe from untoward incidents. Nevertheless, despite the setbacks, the development of innovative products from this space remains strong. Digital assets, alongside a vibrant DeFi ecosystem and robust centralized platforms offering asset storage and exchange activities, among others, are booming. Family Offices Are Showing Unwavering Interest in Digital Assets A rapidly evolving digital asset industry is witnessing massive influxes of funds, now more than ever. With the capabilities it offers to build massive wealth and regulatory perceptions shifting towards the positive, one group of investors flocking to the ecosystem is family offices. Servicing HNWIs (High Net Worth Individuals) and wealthy families to build and maintain generational wealth, family offices are diversifying investment strategies to accommodate digital assets. However, their entry into the crypto markets should occur with the assistance of platforms specializing in handling the large value they pour into these assets. Family offices must turn to working to safeguard users’ assets from the various elements in the ecosystem that can lead to their loss or theft. digital asset custodians Custodians Make Digital Asset Usage Safe and Convenient for Family Offices In fact, jurisdictions rolling out regulations for the asset class are mandating the usage of licensed custodians while dealing with large amounts of assets. And that is for good reason. For one, self-custody wallets aimed to secure digital assets are a good choice for retail users dealing with less significant amounts. On the other hand, interacting with such assets in the millions of dollars, as family offices do, can be inconvenient and dangerous when using self-custody storage solutions. The chances of losing private keys and having assets worth millions trapped forever are more common than assumed and obviously not ideal. Turning to experienced custodians, like BitGo, Liminal, and Copper, is the best way for family offices to go. These custodians offer the perfect balance of security and convenience that they can ask for. Licensed custodians utilize state-of-the-art asset storage and data transfer infrastructure, ensuring the digital assets they handle remain safe. Hot and cold wallets protected by Multi-Signature and are commonplace with custodians, alongside infosec certifications attesting to their robust infrastructure. MPC (Multi-Party Computation) technology Increased Access to the DeFi Ecosystem Through Custody Platforms By turning to custodians, can stay assured they possess instant access to their assets, overseen by bank-grade storage security. Of course, asset storage is only one aspect of custodian offerings. Custodian platforms also come equipped to let their users indulge in DeFi strategies across various blockchain networks – all from their platform interfaces. family offices With the DeFi ecosystem being one of the most prominent use cases of digital assets, family offices naturally utilize the assets under their management to earn steady passive income. So, family offices can indulge in multichain DeFi ecosystems, purchasing all kinds of tokens to put to work in applications deployed on prominent blockchains from well-known third-party custody platforms. Custodian Insurance Removes All Worries of Dealing with Third Parties While relying on third parties to hold crypto assets can seem daunting, especially when blockchain tenets dictate doing away with intermediaries, reputed custodians bring unfaltering trust. It is so because licensed custodians come insured to protect their users from worst-case scenarios like hacks, exploits, and bankruptcies. Family offices can confidently employ custodians, knowing their value is recoverable through insurance despite losses incurred due to negligence or bad behavior exhibited by custodians. Reputed Custodians Uphold Compliance Even for Their Clients Numerous jurisdictions with established digital asset regulations enforce insurance on custodians to safeguard investors. They also enforce AML (Anti-Money Laundering) and CFT (Countering the Financing of Terrorism) protocols upon these platforms, which is a need in the digital asset ecosystem. Such protocols prevent family offices from accidentally indulging in prohibited tokens like privacy coins and interacting with sanctioned wallets linked with illicit activities. Tapping into custody services can make sure family offices remain within legal bounds and do not have their operations, funds, and investments jeopardized. Likewise, custodians can even effect good behavior among family offices, mainly multi-family offices handling investments for multiple HNWIs and families. This type of family office is operated by third parties, rather than the traditional single-family office set up and controlled by HNWIs or families themselves. Most jurisdictions have lax regulations over family offices, meaning the clients of multi-family offices will find their value at risk if the third parties misappropriate said value. Custodians storing end of such offices are mandated to track and report suspicious movements of funds by regulators. client digital assets on behalf Malicious activity can easily be detectable when huge sums are misused, protecting investors from bad actors. Thereby, custodians offer a layer of transparency and accountability. Multi-family offices looking to increase their credibility with end clients interested in digital assets must employ custodians. Custody Solutions Are a Necessity for Crypto-Inclined Family Offices Digital asset adoption is in full swing as investors turn to the asset type and the innovative products it is central to. Family offices are the same as these assets, as they have shown their ability to steadily bring returns that magnify wealth, often more effectively than traditional assets. As they take a more influential place in the portfolios of HNWIs and wealthy families, custodians can help alleviate the issues with handling them. Thus, family offices should consider custody-related solutions to traverse the while making the most out of the novel financial system. digital asset landscape securely and conveniently

Why Family Offices Should Care About Digital Asset Custody

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. : Authors (1) Akash Chikhalikar, Graduate School of Engineering, Department of Robotics, Tohoku University; (2) Ankit A. Ravankar, Graduate School of Engineering, Department of Robotics, Tohoku University; (3)  Jose Victorio Salazar Luces, Graduate School of Engineering, Department of Robotics, Tohoku University; (4) Yasuhisa Hirata, Graduate School of Engineering, Department of Robotics, Tohoku University. Table of Links Abstract & Introduction Related Work Semantic Mapping Framework Multi-Target Search Experimental Studies Conclusions and Discussions, Acknowledgment and References Abstract In recent years, the demand for service robots capable of executing tasks beyond autonomous navigation has grown. In the future, service robots will be expected to perform complex tasks like ‘Set table for dinner’. High-level tasks like these, require, among other capabilities, the ability to retrieve multiple targets. This paper delves into the challenge of locating multiple targets in an environment, termed ‘Find my Objects.’ We present a novel heuristic designed to facilitate robots in conducting a preferential search for multiple targets in indoor spaces. Our approach involves a Semantic SLAM framework that combines semantic object recognition with geometric data to generate a multi-layered map. We fuse the semantic maps with probabilistic priors for efficient inferencing. Recognizing the challenges introduced by obstacles that might obscure a navigation goal and render standard point-to-point navigation strategies less viable, our methodology offers resilience to such factors. Importantly, our method is adaptable to various object detectors, RGB-D SLAM techniques, and local navigation planners. We demonstrate the ‘Find my Objects’ task in realworld indoor environments, yielding quantitative results that attest to the effectiveness of our methodology. This strategy can be applied in scenarios where service robots need to locate, grasp, and transport objects, taking into account user preferences. I. INTRODUCTION Service robots, now ubiquitous in domestic environments, are being deployed for diverse array of applications ranging object delivery, patrolling, surveillance, cleaning, and monitoring. Additionally, advancements in techniques such as SLAM (simultaneous Localization and Mapping), deep learning architectures, path planning algorithms, and object manipulation have accelerated recent development [1]. Their integration spans a spectrum of use-cases, from automating repetitive household chores to specialized long-term care applications for the elderly [2], [3]. However, one fundamental skill that all service robots require is the ability to retrieve items effectively. Relying solely on exhaustive exploration or random navigation can be energy-intensive and may not meet time requirements. Therefore, it is essential to harness diverse information sources to refine robotic object search. Semantic SLAM can provide significant support in this regard. Semantic SLAM involves the extraction and integration of semantic understanding with geometric data to produce detailed, multi-layered maps. These maps not only identify landmarks like furniture and appliances typical in a house setting but also classify areas such as living rooms [4], [5]. In addition, state-of-the-art semantic SLAM approaches incorporate beliefs about dynamic entities such as other agents or humans [6]. With the evolution of deep neural networks, extracting semantic information has become more streamlined. Convolutional Neural Networks (CNN) like Yolov7 [7] facilitate real-time object detection, while models like MaskRCNN [8] enable detailed instance segmentation. By leveraging semantic SLAM and deep learning, service robots can enhance object search capabilities. Despite significant advancements in robotics, robots today still lack the intuitive scene-awareness that humans naturally possess. For instance, while a robot can identify a kitchen and a cup, it should also understand that ‘cups are typically found in kitchens’. This intuitive knowledge and place object associations can be captured using ontologies [9], [10]. Therefore, blending information from probabilistic priors with semantic maps is crucial for an efficient search. In order to execute further higher level tasks such as ‘Set table for dinner’ or ‘Prepare bag for work’, robots will have to efficiently plan and retrieve multiple items in the environment. Given these considerations, our research aims to address multi-target search in indoor settings, termed the task. We have designed a framework that can efficiently explore semantic maps to look for commonly occurring items in daily life. The main contributions of this paper include: ‘Find my Objects’ • Defining a multi-target search challenge in indoor settings with adaptable region-to-region navigation. • A framework to integrate probabilistic priors with multilayered semantic maps for search. • A novel heuristic to prioritize targets based on user input/demands. • Quantitative results obtained in real environments and comparisons of our proposed heuristic with baselines for object search. The remainder of the paper is structured as follows. Section II reviews related work and highlights our contributions. Section III details our methodology to create a multi-layered semantic map. Section IV discusses the multi-target search algorithm. Section V presents our experiments, quantitative results, and analysis of the observed data. Finally, Section VI concludes the paper with a discussion.

Preferential Multi-Target Search in Indoor Environments using Semantic SLAM: Abstract & Intro

De.Fi,the premier Web3 Super App, continues its robust expansion in the dynamic crypto space. After evolving into the most extensive all-in-one platform with a trusted user base of 5M globally, De.Fi is thrilled to unveil the official start of Airdrop Season 1. DUBAI,the United Arab Emirates, February 15th, 2024/Chainwire/-- De.Fi (DEFI) is taking a stride forward in Web3 innovation and has just declared the . This announcement comes as a celebration of over four years of De.Fi's transformative contributions to the ever-evolving crypto landscape and a heartfelt expression of gratitude to their loyal De.Fi Army. launch of its first Airdrop season In Season 1, De.Fi has airdropped a staggering 3,800,000 DEFI tokens. Rewards were divided between 2 main categories: De.Fi SocialFi and Diamond Hands Airdrops. Some users received over $8,000 in Airdrop! Since its inception in the summer of 2020, the De.Fi team has diligently worked on advancing their project and earning a reputation as a Web3 Security Giant. De.Fi is backed by a range of and prominent investors from around the world, including 21Shares, Shima Capital, HOF Capital, and other leading VCs. leading Venture Capital firms Over the four years of development De.Fi successfully grew its user base by 100X, built a multifunctional crypto platform with unique security tools, identified and defused over 540,000 risk tokens, and potentially saved over $27 billion. John Izaguirre, the Former Director of Binance Chain, says: “Having closely followed the De.Fi Team for years, I can attest to their transformative impact on the cryptocurrency landscape. Their game-changing Web3 SuperApp & Crypto Antivirus set a new standard for enhancing Crypto Security. I will be looking forward to the unfolding of their future innovations in the dynamic world of DeFi!” The team has officially shared , revealing that the community will enjoy more airdrop campaigns in the near future. Airdrop Season 1 marks the beginning of an exciting chapter, promising a series of rewarding events for De.Fi’s users. exciting news Staking DEFI from the Airdrop Season 1 will be the primary path for qualifying for the next Airdrop Seasons and unlocking the maximum of DEFI , including: token utilities De.Fi Fees Removal De.Fi Product Discounts Tickets to the De.Fi Launchpad Loyalty Program Partner Benefits Access to Product About De.Fi De.Fi is a Web 3 Super App because it offers a number of unique and valuable features. This all-in-one platform serves as a portfolio manager dashboard, facilitating tracking of crypto and DeFi positions across a large selection of chains, including non-EVM. Navigate through the feature to find the best DeFi opportunities effortlessly. The , an innovative security system embedded in the dashboard, diligently alerts users to risky tokens and smart contracts. Explore Yields Crypto Antivirus De.Fi is gamifying the onboarding process of users into Web3, allowing them to create profiles, amass followers, build communities, earn points, and unlock rewards. SocialFi Contact PR Manager Maria Lyshchuk De.Fi marketing@de.fi This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

De.Fi Awards Over $8,000 To Users In Successful Airdrop, Fuels Web3 Growth

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Carlos Efrain Quintero-Narvaez, School of Science and Engineering Tecnologico de Monterrey; (2) Raul Monroy-Borja, School of Science and Engineering Tecnologico de Monterrey. TABLE OF LINKS Abstract and Introduction Background Design Discussion Conclusions and References Abstract We present an implementation of a Web3 platform that leverages the Groth16 Zero-Knowledge Proof schema to verify the validity of questionnaire results within Smart Contracts. Our approach ensures that the answer key of the questionnaire remains undisclosed throughout the verification process, while ensuring that the evaluation is done fairly. To accomplish this, users respond to a series of questions, and their answers are encoded and securely transmitted to a hidden backend. The backend then performs an evaluation of the user’s answers, generating the overall result of the questionnaire. Additionally, it generates a Zero-Knowledge Proof, attesting that the answers were appropriately evaluated against a valid set of constraints. Next, the user submits their result along with the proof to a Smart Contract, which verifies their validity and issues a non-fungible token (NFT) as an attestation of the user’s test result. In this research, we implemented the Zero-Knowledge functionality using Circom 2 and deployed the Smart Contract using Solidity, thereby showcasing a practical and secure solution for questionnaire validity verification in the context of Smart Contracts. —zero-knowledge, proof, web3, circom, questionnaire, solidity, smart contract, blockchain Index Terms I. INTRODUCTION Blockchain technologies and Smart Contracts have gained significant traction recently, offering transparent and decentralized solutions to problems in different domains. Doing trustless verification and attestation of facts while preserving privacy is one of these domains. In particular, verification of questionnaire evaluation results presents a unique challenge, as this calls for preserving confidentiality of privileged information such as the answers to the questionnaire. This paper introduces an implementation of a Web3 platform that leverages Zero-Knowledge Proofs (ZKPs) and Smart Contracts to address this challenge. By employing the Groth16 Zero-Knowledge Proof schema, we establish a robust framework that enables users to receive proofs of the validity of their test results without revealing the underlying evaluation methods or answers. Furthermore, users can then produce attestations of their results in the form of Non-Fungible Tokens (NFTs) generated by a Smart Contract that verifies the ZKP we just mentioned. This way, we obtain an end-to-end questionnaire answering, validating and attesting protocol. The significance of this research lies in enhancing trust and reliability in the evaluation of questionnaire-based assessments. By leveraging ZKPs in this way, test appliers can securely show that their evaluations are fair without compromising the integrity of the test. In turn, users can obtain reliable evidence of their performance in these assessments and share it with third parties without worrying about suspicions of forgery or other dishonest practices. We provide a comprehensive overview of our implementation, including the design and architecture of the Smart Contracts using Solidity and the implementation details using Circom 2 for the ZKP functionality. Additionally, we present the evaluation results, discussing the system’s performance, security, and privacy aspects. By introducing this innovative approach to questionnaire validity verification in smart-contracts, we aim to open avenues for integrity-preserving attestation mechanisms in a range of applications. The subsequent sections delve into the background, methods, results, and discussion, highlighting the significance and potential implications of our research, as well as future directions for improvement. II. BACKGROUND A. Blockchain and Smart Contracts Blockchain technologies are being increasingly adopted in different domains for their features regarding transparency and decentralization. Bitcoin [8] was the first proposed protocol based on this architecture, combining in its functioning techniques such as Proof-of-Work (PoW) [3], SHA-256 hashing, Merkle Trees, and the Elliptic Curve Digital Signature Algorithm (ECDSA). It is specially powerful because of its decentralized nature, provided through the Proof-of-Work validation done with each block of transactions added to a public registry known as the Blockchain. In the following years, variations on the protocol proposed by Bitcoin appeared, collectively known as cryptocurrencies, each having their own blockchain where the transactions are registered. Ethereum [4], Monero [12], Polkadot [13], Polygon [6], NEAR, Solana [14] and Avalanche [11] are some examples of these derivate protocols. Of the protocols we just mentioned, Ethereum is a critical one to talk about as it was the first to work with the Ethereum Virtual Machine (EVM) [4] architecture, an essential feature on which many other blockchains are built upon. The EVM allows protocols to execute code in a decentralized manner and register all of its execution steps on the blockchain, making the results of that code transparent and unbiased. This code is then deployed to an address stored in the blockchain and referred to as a Smart Contract, i.e. a contract that enforces itself automatically. A simple example of a task that can be performed using a Smart Contract is that of currency exchange. Usually, when two entities, Alice and Bob, each holding a different digital currency, want to exchange one for the other at a certain rate, a level of trust is needed. Indeed, either Alice has to transfer its currency to Bob first or vice versa, making the first entity that transfers vulnerable to the other not fulfilling their part of the deal. One way to balance this trust is by introducing a third-party entity, Charly, to which both parties transfer their funds so that Charly is tasked with ensuring that the deal is fulfilled. However, this only changes the protocol so that now both Alice and Bob have to trust Charly to act correctly. Here is where Smart Contracts come in, instead of having the parties transfer to a third one, transfer to a Smart Contract entity that automatically verifies that the deal is fulfilled without Alice and Bob having to trust in it, as it is executed on a decentralized network. This is illustrated in Figure 1. These trustless features of Smart Contracts then lead the way to a variety of applications to be built. Decentralized Finance (DeFi) [1], Automated Market Makers [2], and decentralized social media [10], are among the many uses of Smart Contracts that have developed in recent years. Once again, the decentralized and trustless nature in the execution of the code for each of these applications is what allows them to provide an enhanced trustless performance in comparison to their traditional counterparts. As we have said before, the essential base on which the Smart Contract infrastructure is built is the Blockchain. Let us note some of the implications of one particular attribute of the Blockchain, its functioning as a public registry. Every time a new block of valid transactions appears, it needs to be “mined”, i.e. finding a valid “nonce” value that when added at the end of the transaction block makes its SHA256 hash have certain characteristics. These characteristics are often that the hash has a number of zeros at the beginning, a number determined by the “difficulty” imposed by the network. This mining process is non-trivial by design, so that “work” needs to be performed before adding any transaction to the public ledger, hence the Proof-of-Work name. Notice that in order for the network to reach consensus that the work has been performed and the transactions validated accordingly, it is necessary for everyone to have complete access to each new transaction block. This makes any transfer of funds in the Blockchain public, including its receivers and senders. For this reason, it would be convenient to have a method through which the network could validate transactions without receiving compromising knowledge about its users, this is where Zero-Knowledge Proofs come in to play. B. Zero-Knowledge Proofs In the context of Blockchain transactions, one problem that arises is that, due to the public consensus nature of the protocol, all transactions are public. It is straightforward to check the movements of an entities funds by just checking the Blockchain registry. Although this can be partially avoided due to the easiness with which one can create new addresses (if an entity’s address is disclosed, it can just send its funds to a new one), it is still possible to just trace back the origins of any funds. A possible solution to this could work if there was a way for the network to verify the validity of transactions without them knowing the sender or the receiver, i.e. if there was a way to prove that a transaction is valid without disclosing any additional knowledge. Indeed, Zero-Knowledge Proofs are capable of doing this, as we will explaint next. A Zero-Knowledge Proof (ZKP) is a method through which one entity can prove a statement to another one without disclosing more information than the fact that particular statement is true. For example, given a fixed function f : X −→ Y and a fixed value y ∈ Y . If we wanted to prove to a third party that we know a value x ∈ X such that f(x) = y, a trivial solution would be to just disclose the value of x we know. However, that shares more information than the fact that the statement is true. A ZKP would entail generating a proof generator function P and a proof verifier V . Such that we can generate a witness w = P(x) that does not disclose the value x, and that another entity can then evaluate with the verifier function V (w) and obtain a true or false value that determines whether the witness was created from a valid solution or not. This way, we can prove to another entity that we know a value of x such that f(x) = y, while disclosing zero knowledge about which specific value of x we know. For a more intuitive example, consider a hashing function hash : I −→ H from an input space I to a hash space H, this function could be SHA-256, SHA-1, etc. Also, consider two entities, Alice A and Bob B. Imagine that Alice guards an entrance to a treasure cave, to which one can only enter by having a password ρ that is verified using its hash, i.e. Alice has the hash of the password h = hash(ρ). However, Alice herself does not have the password and cannot enter the cave. Now consider that Bob does have the password ρ and wants to enter the cave, but he does not want to disclose the password to Alice as to not have to share it with her. For most use cases, this can be achieved by having Bob compute the hash of ρ, and show it to Alice, having her verify that it is the same as h, the one she has. However, say that h is not disclosed to Alice through a private channel but through some public method as to ensure that she verifies passwords correctly and does not commit any dishonesty. This renders just showing the hash value h of the password not a valid method for verification. Usually, the only method left would be for Bob to disclose ρ to Alice and have her compute its hash, but there is another way to do this, which involves Zero-Knowledge Proofs. Groth16[5] is a Zero-Knowledge Proof system designed to generate a proof-verifier pairing schema for satisfiability of any arithmetic circuit. What is important about the arithmetic circuit satisfiability problem is the fact that it is NP-Complete and thus allows for a wide arrange of statements to be translated into that form, including those which are often of interest in the context of ZKPs. Note that, arithmetic circuits are comprised of gates that compute arithmetic operations (addition and multiplication) on a field F, with wires connecting the gates to represent results from one going to another. Thus, recall the SAT problem, which is NP-Complete and formulated as “given a statement with boolean variables, is it possible to find an assignment for them such that the statement is true?”. Similarly, the arithmetic circuit satisfiability problem is NPComplete too and formulated as “given a statement with variables with values from a field F, is it possible to find an assignment for them such that the statement is true?”. This makes Groth16 an ideal system for working when generating ZKPs, being used for ZK languages like Circom 2 [7]. Coming back to our conundrum with Alice and Bob in the treasure cave. As it happens, hashing functions such as SHA256 can be expressed in terms of arithmetic circuit satisfiability and can thus have a corresponding Zero-Knowledge Proof generated. Therefore, in the situation we had, it is enough for Alice to have a SHA-256 ZK proof verifier V and for Bob to use the prover function P to generate a witness w = P(ρ). Bob then shares w with Alice, so she evaluates it with the verifier V (w) and determines that the witness is valid. Thus, Alice can be sure that Bob does have the password without receiving any information about what is its exact value. Finally, it is straightforward to see how this can be applied to Blockchain transaction verification. Suppose that we have a transaction t represented in some space T, along with a validation function v that tells whether t is valid with the current state of the Blockchain, having 0 or 1 as output. Then for the transaction to be added by a miner into the registry, it would suffice to send a proof that v(t) = 1. Then, we could translate the computation of v into the Groth16 schema by using a compiler such as Circom 2.This would then allow us to generate a proof-verifier pair P and V . And finally, as was done with the Alice and Bob case, the sender would just generate a witness of the transaction w = P(t) and send it to the miners which would verify it by evaluating V (w). This schema is used by cryptocurrencies such as Monero and Zcash. Now, see that in the context that concerns this paper, a ZKP could be generated for the validity of the evaluation of a questionnaire according to some set of constraints. This would allow the user or any third party to get a convincing proof of the evaluation having been performed fairly, without revealing any information about the answers that give certain results. III. DESIGN Our implementation makes heavy usage of Circom 2 and its features for generating Solidity code for ZK proof verifiers. This code is then integrated into an ERC-721 Smart Contract that allows the user to mint an NFT when a result of the questionnaire with the corresponding proof is provided. A basic implementation was made for a new Web3 platform called P3rsonalities, intended to have a personality test with results validated with a ZK proof and attested through a generated ERC-721 NFT. The deployed contract code with the Circom 2 generated Solidity verifier can be found at the P3rsonalities GitHub Repository. We designed the Circom 2 code in such a way that it receives a two bit masks and one integer as inputs, one bit mask representing the user’s answers for each question, the other representing the answer key, and the integer representing the result of the test, having a total of 10 questions. The questions are divided into two groups so that each one represents an attribute of the final result, i.e. the final result will be a two bits integer, each bit representing an attribute. After compiling, Circom 2 generates two files we use, one WebAssembly script for the generation of the ZK witness and another Solidity script for verification of said witness on a Smart Contract executed on an EVM Blockchain. It is important to note that the generated Solidity code makes heavy use of the assembly functionalities available on the EVM, as to ensure that gas costs for executing the verification are as low as possible. The witness generator along with the questionnaire evaluator are deployed to a centralized server, in this case an AWS Lambda function, for easy deployment and access from a REST API endpoint. The user then makes a request to this API, sending its answers to the questionnaire. The API then executes the witness generator code and returns the result of the test together with the ZK witness. The user then makes a call, with the generated witness as an input, to the deployed ERC-721 Smart Contract for minting the NFT, attesting the result returned by the API. This Smart Contract is modified so that the Solidity verifier script generated by Circom 2 is used as a required check before minting the NFT to the user’s address. At the end of the procedure, the user obtains a Soulbound NFT (an NFT that cannot be transferred) representing the result of their answers to the questionnaire. As this NFT can only be generated when the user possesses a valid witness for that result, it holds a special value as evidence for anyone interested in verifying the results of such a test. This paper is under CC 1.0 license. available on arxiv

Zero-Knowledge Proofs: Ensuring Privacy in Smart Contract Questionnaire Validity Verification

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